scholarly journals Otolith chemistry discriminates water mass occupancy of Arctic fish in the Chukchi Sea

2016 ◽  
Vol 67 (7) ◽  
pp. 967 ◽  
Author(s):  
Christine M. Gleason ◽  
Brenda L. Norcross ◽  
Karen J. Spaleta
Keyword(s):  
Bottom Water ◽  
Water Mass ◽  
Water Masses ◽  
Polar Cod ◽  
Correct Classification ◽  
Age Classes ◽  
Bottom Water Temperature ◽  
Arctic Cod ◽  
Staghorn Sculpin

The microchemistry of otoliths has the potential to reconstruct fish movement patterns and habitat use between environmentally different habitats for individual age classes of Arctic marine fish. Herein, we tested the relationship between the bottom water mass from which a fish was collected and the microchemistry of the most recent growth edge of the fish’s otolith using Mg, Sr, Ba and Ca, and then determined the physical and biological factors that affected the chemical signatures. A discriminant function post hoc analysis of fish occupying bottom water masses resulted in 76% correct classification of Arctic or Polar cod (Boreogadus saida) and 82% correct classification of Arctic staghorn sculpin (Gymnocanthus tricuspis) into bottom water masses of capture when ages were pooled. By separating age classes, correct classifications into water masses of capture were as high as 87% for Arctic cod (three water masses) and 90% for Arctic staghorn sculpin (two water masses). Otolith Ba:Ca, Mg:Ca and Sr:Ca ratios were most consistently affected by bottom water temperature; the latter two were also affected by fish age and fish length. The use of otolith microchemistry to determine occupancy of water masses over time is most promising for Arctic cod, which is widespread and occupies the most thermally diverse habitats in Arctic waters.

scholarly journals Water masses in the Atlantic Ocean: characteristics and distributions

Ocean Science ◽  
2021 ◽  
Vol 17 (2) ◽  
pp. 463-486
Author(s):  
Mian Liu ◽  
Toste Tanhua
Keyword(s):  
Atlantic Ocean ◽  
Deep Water ◽  
Bottom Water ◽  
Water Mass ◽  
Sea Water ◽  
Weddell Sea ◽  
Water Masses ◽  
Global Ocean ◽  
Intermediate Water ◽  
The North

Abstract. A large number of water masses are presented in the Atlantic Ocean, and knowledge of their distributions and properties is important for understanding and monitoring of a range of oceanographic phenomena. The characteristics and distributions of water masses in biogeochemical space are useful for, in particular, chemical and biological oceanography to understand the origin and mixing history of water samples. Here, we define the characteristics of the major water masses in the Atlantic Ocean as source water types (SWTs) from their formation areas, and map out their distributions. The SWTs are described by six properties taken from the biased-adjusted Global Ocean Data Analysis Project version 2 (GLODAPv2) data product, including both conservative (conservative temperature and absolute salinity) and non-conservative (oxygen, silicate, phosphate and nitrate) properties. The distributions of these water masses are investigated with the use of the optimum multi-parameter (OMP) method and mapped out. The Atlantic Ocean is divided into four vertical layers by distinct neutral densities and four zonal layers to guide the identification and characterization. The water masses in the upper layer originate from wintertime subduction and are defined as central waters. Below the upper layer, the intermediate layer consists of three main water masses: Antarctic Intermediate Water (AAIW), Subarctic Intermediate Water (SAIW) and Mediterranean Water (MW). The North Atlantic Deep Water (NADW, divided into its upper and lower components) is the dominating water mass in the deep and overflow layer. The origin of both the upper and lower NADW is the Labrador Sea Water (LSW), the Iceland–Scotland Overflow Water (ISOW) and the Denmark Strait Overflow Water (DSOW). The Antarctic Bottom Water (AABW) is the only natural water mass in the bottom layer, and this water mass is redefined as Northeast Atlantic Bottom Water (NEABW) in the north of the Equator due to the change of key properties, especially silicate. Similar with NADW, two additional water masses, Circumpolar Deep Water (CDW) and Weddell Sea Bottom Water (WSBW), are defined in the Weddell Sea region in order to understand the origin of AABW.

scholarly journals Distribution Trend of the Bottom Water at the Bay Mouth of Jiaozhou Bay

2019 ◽  
Vol 136 ◽  
pp. 06013
Author(s):  
Dongfang Yang ◽  
Haoyuan Ren ◽  
Dong Yang ◽  
Haixia Li ◽  
Jing Fang
Keyword(s):  
High Temperature ◽  
Water Temperature ◽  
Bottom Water ◽  
Water Mass ◽  
Jiaozhou Bay ◽  
Temperature Zone ◽  
Bottom Water Temperature ◽  
June July August ◽  
June July ◽  
July August September

Based on the survey data of Jiaozhou Bay in May, June, July, August, September and October of 1980, the bottom water temperature and its horizontal distribution in Jiaozhou Bay were studied. The results showedthat the bottom water temperaturein Jiaozhou Bay rangedat a high level between 12.35℃to 25.72℃and a low level between 10.18℃to 24.58 ℃in May, June, July, August, September and October. From May to October, the bottom water temperature in Jiaozhou Bay was moderately high. In May, June, July and August, a high temperature zone formed around the waterinside the bay mouth, and the bottom water temperaturereached 12.35℃to 25.72℃.From May to August, the bottom water temperaturefirst increased in the watersinside the bay mouth, followed by the water at the bay mouth, withthe water outside the bay mouthas the end. In September and October, the temperature of the eastern coastal water outside Jiaozhou Bay ranged from 20.00℃to 24.43℃, and a high temperature zone formed around there. From September to October,the bottom water temperaturefirst decreased in the water inside the bay mouth, followed by the water at the bay mouth, with the water outside the bay mouthas the end. According to Yang Dongfang's definition of “Cryogenic Low Water Mass”, a cryogenic water mass formed in the bottom water at the bay mouthin September and extended widely among the water inside the bay mouth-at the bay mouth-in the southern part outside the bay mouthwith a temperature of 23.79℃to 23.91℃.

scholarly journals Distribution of Water Masses in the Atlantic Ocean based on GLODAPv2

2019 ◽  
Author(s):  
Mian Liu ◽  
Toste Tanhua
Keyword(s):  
Atlantic Ocean ◽  
Bottom Water ◽  
Water Mass ◽  
Potential Temperature ◽  
Weddell Sea ◽  
Water Masses ◽  
General Distribution ◽  
Intermediate Water ◽  
The North ◽  
The Antarctic

Abstract. The distribution of the main water masses in the Atlantic Ocean are investigated with the Optimal Multi-Parameter (OMP) method. The properties of the main water masses in the Atlantic Ocean are described in a companion article; here these definitions are used to map out the general distribution of those water masses. Six key properties, including conservative (potential temperature and salinity) and non-conservative (oxygen, silicate, phosphate and nitrate), are incorporated into the OMP analysis to determine the contribution of the water masses in the Atlantic Ocean based on the GLODAP v2 observational data. To facilitate the analysis the Atlantic Ocean is divided into four vertical layers based on potential density. Due to the high seasonal variability in the mixed layer, this layer is excluded from the analysis. Central waters are the main water masses in the upper/central layer, generally featuring high potential temperature and salinity and low nutrient concentrations and are easily distinguished from the intermediate water masses. In the intermediate layer, the Antarctic Intermediate Water (AAIW) from the south can be detected to ~30 °N, whereas the Subarctic Intermediate Water (SAIW), having similarly low salinity to the AAIW flows from the north. Mediterranean Overflow Water (MOW) flows from the Strait of Gibraltar as a high salinity water. NADW dominates the deep and overflow layer both in the North and South Atlantic. In the bottom layer, AABW is the only natural water mass with high silicate signature spreading from the Antarctic to the North Atlantic. Due to the change of water mass properties, in this work we renamed to North East Antarctic Bottom Water NEABW north of the equator. Similarly, the distributions of Labrador Sea Water (LSW), Iceland Scotland Overflow Water (ISOW), and Denmark Strait Overflow Water (DSOW) forms upper and lower portion of NADW, respectively roughly south of the Grand Banks between ~50 and 66 °N. In the far south the distributions of Circumpolar Deep Water (CDW) and Weddell Sea Bottom Water (WSBW) are of significance to understand the formation of the AABW.

Foraminifera–mollusc associations in eastern Chaleur Bay

1978 ◽  
Vol 15 (6) ◽  
pp. 889-901 ◽  
Author(s):  
C. T. Schafer ◽  
F. J. E. Wagner
Keyword(s):  
High Temperature ◽  
Water Temperature ◽  
Shallow Water ◽  
Marine Sediments ◽  
Bottom Water ◽  
Water Mass ◽  
Cold Water ◽  
Bottom Water Temperature ◽  
Mollusc Species ◽  
Deep Bay

Common associations of mollusc and Foraminifera species were investigated in eastern Chaleur Bay, Gulf of St. Lawrence. Three depth-related biotopes are recognized. The distribution of species in this open bay environment appears to be controlled by substrate and (or) water mass characteristics. North–south differences in shallow water assemblages are related to summer bottom water temperature; calcareous Foraminifera and molluscs dominate in relatively high temperature environments. The influx of cold water (<1 °C) at intermediate depths (40–80 m) is reflected by an increase in the abundance of arenaceous Foraminifera species such as Reophax scottii, and by the absence of numerous mollusc species that are found at these depths elsewhere. A deep bay biotope (>80 m) can be recognized primarily on the basis of the mollusc species Yoldia limatula and Periploma fragile in association with the Foraminifera species Islandiella islandica. The observed mollusc–Foraminifera associations can be applied to paleoenvironmental and biostratigraphic studies of north temperate Holocene marine sediments.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Reliability of equine visual lameness classification as a function of expertise, lameness severity and rater confidence

2018 ◽  
Vol 184 (2) ◽  
pp. 63-63 ◽  
Author(s):  
Sandra Dorothee Starke ◽  
Maarten Oosterlinck
Keyword(s):  
Diagnostic Accuracy ◽  
Three Dimensional ◽  
Chance Level ◽  
Correct Classification ◽  
Full Understanding ◽  
Case Load ◽  
Movement Asymmetry ◽  
Load Exposure ◽  
Hindlimb Lameness

Visual equine lameness assessment is often unreliable, yet the full understanding of this issue is missing. Here, we investigate visual lameness assessment using near-realistic, three-dimensional horse animations presenting with 0–60 per cent movement asymmetry. Animations were scored at an equine veterinary seminar by attendees with various expertise levels. Results showed that years of experience and exposure to a low, medium or high case load had no significant effect on correct assessment of lame (P>0.149) or sound horses (P≥0.412), with the exception of a significant effect of case load exposure on forelimb lameness assessment at 60 per cent asymmetry (P=0.014). The correct classification of sound horses as sound was significantly (P<0.001) higher for forelimb (average 72 per cent correct) than for hindlimb lameness assessment (average 28 per cent correct): participants often saw hindlimb lameness where there was none. For subtle lameness, errors often resulted from not noticing forelimb lameness and from classifying the incorrect limb as lame for hindlimb lameness. Diagnostic accuracy was at or below chance level for some metrics. Rater confidence was not associated with performance. Visual gait assessment may overall be unlikely to reliably differentiate between sound and mildly lame horses irrespective of an assessor’s background.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals Reconstructing bottom water temperatures from measurements of temperature and thermal diffusivity in marine sediments

2014 ◽  
Vol 11 (5) ◽  
pp. 2391-2422
Author(s):  
F. Miesner ◽  
A. Lechleiter ◽  
C. Müller
Keyword(s):  
Steady State ◽  
Heat Flow ◽  
Water Temperature ◽  
Marine Sediments ◽  
Bottom Water ◽  
Measured Data ◽  
Temperature History ◽  
Artificial Data ◽  
Bottom Water Temperature ◽  
Steady State Heat

Abstract. Temperature fields in marine sediments are studied for various purposes. Often, the target of research is the steady state heat flow as a (possible) source of energy but there are also studies attempting to reconstruct bottom water temperature variations to understand more about climate history. The bottom water temperature propagates into the sediment to different depths, depending on the amplitude and period of the deviation. The steady state heat flow can only be determined when the bottom water temperature is constant while the bottom water temperature history can only be reconstructed when the deviation has an amplitude large enough or the measurements are taken in great depths. In this work, the aim is to reconstruct recent bottom water temperature history such as the last two years. To this end, measurements to depths of up to 6 m shall be adequate and amplitudes smaller than 1 K should be reconstructable. First, a commonly used forward model is introduced and analyzed: knowing the bottom water temperature deviation in the last years and the thermal properties of the sediments, the forward model gives the sediment temperature field. Next, an inversion operator and two common inversion schemes are introduced. The analysis of the inversion operator and both algorithms is kept short, but sources for further reading are given. The algorithms are then tested for artificial data with different noise levels and for two example data sets, one from the German North Sea and one from the Davis Strait. Both algorithms show good and stable results for artificial data. The achieved results for measured data have low variances and match to the observed oceanographic settings. Lastly, the desired and obtained accuracy are discussed. For artificial data, the presented method yields satisfying results. However, for measured data the interpretation of the results is more difficult as the exact form of the bottom water deviation is not known. Nevertheless, the presented inversion method seems rather promising due to its accuracy and stability for artificial data. Continuing to work on the development of more sophisticated models for the bottom water temperature, we hope to cover more different oceanographic settings in the future.

scholarly journals Application of neuron networks of deep learning for exposures editing of digital phonograms

2020 ◽  
Vol 65 (4) ◽  
pp. 506-512
Author(s):  
V. I. Solovyov ◽  
O. V. Rybalskiy ◽  
V. V. Zhuravel ◽  
V. K. Zheleznyak
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Maximum Size ◽  
Maximum Efficiency ◽  
Correct Classification ◽  
Digital Audio ◽  
Neuron Networks ◽  
Effective System ◽  
Further Development

Possibility of creation of effective system, which is intended for exposure of tracks of editing in digital phonograms and is built on the basis of neuron networks of the deep learning, is experimentally proven. Sense of experiment consisted in research of ability of the systems on the basis of such networks to expose pauses with tracks of editing. The experimental array of data is created in a voice editor from phonograms written on the different apparatus of the digital audio recording (at frequency of discretisation 44,1 kHz). A preselection of pauses was produced from it, having duration from 100 мs to a few seconds. From 1000 selected pauses the array of fragments of pauses is formed in the automatic (computer) mode, from which the arrays of fragments of pauses of different duration are generated by a dimension about 100 000. For forming of array of fragments of pauses with editing, the chosen pauses were divided into casual character parts in arbitrary correlation. Afterwards, the new pauses were created from it with the fixed place of editing. The general array of all fragments of pauses was broken into training and test arrays. The maximum efficiency, achieved on a test array in the process of educating, was determined. In general case this efficiency is determined by the maximum size of probability of correct classification of fragments with editing and fragments without editing. Scientifically reasonable methodology of exposure of signs of editing in digital phonograms is offered on the basis of neuron networks of the deep learning. The conducted experiments showed that the construction of the effective system is possible for the exposure of such tracks. Further development of methodology must be directed to find the ways to increase the probability of correct binary classification of investigated pauses.

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