‘Batteries’ in Machine Learning: A First Experimental Assessment of Inference for Siberian Crane Breeding Grounds in the Russian High Arctic Based on ‘Shaving’ 74 Predictors

2018 ◽  
pp. 163-184 ◽  
Author(s):  
Falk Huettmann ◽  
Chunrong Mi ◽  
Yumin Guo
Keyword(s):  
Machine Learning ◽  
High Arctic ◽  
Experimental Assessment ◽  
Siberian Crane ◽  
Breeding Grounds

scholarly journals Transformations at High Latitudes: Why do Red Knots Bring Body Stores to the Breeding Grounds?

The Condor ◽  
2005 ◽  
Vol 107 (2) ◽  
pp. 449-457 ◽  
Author(s):  
R. I Guy Morrison ◽  
Nick C. Davidson ◽  
Theunis Piersma
Keyword(s):  
Physiological State ◽  
High Arctic ◽  
Egg Laying ◽  
Concomitant Increase ◽  
Rapid Loss ◽  
Major Function ◽  
Calidris Canutus ◽  
Egg Formation ◽  
Breeding Grounds ◽  
Gut Length

Abstract We examined changes in body composition of Red Knots (Calidris canutus islandica) following arrival on their High Arctic breeding grounds at Alert, Ellesmere Island, Canada. Knots arrived in late May and early June with large fat and muscle stores. In the next two weeks, fat and protein stores (pectoral muscles) declined, while increases occurred in gizzard, proventriculus, gut length, heart, liver, and possibly gonads. Most stores were used before egg laying occurred and were therefore not available for egg formation. Early development of ova in some females suggests that body stores may be incorporated into the earliest eggs. While stores may be used for survival when conditions are difficult after arrival, their rapid loss and the concomitant increase in other organs suggests that a major function may be to facilitate a transformation from a physiological state suitable for migration to one suitable, and possibly required, for successful breeding. Transformaciones a Altas Latitudes: ¿Por qué Calidris canutus islandica Lleva Reservas Corporales a los Sitios de Nidificación? Resumen. Examinamos los cambios en la condición corporal de individuos de Calidris canutus islandica luego de que éstos llegaran a sus territorios reproductivos a altas latitudes en el Ártico en Alert, isla Ellesmere, Canadá. Los individuos de C. c. islandica llegaron a fines de mayo y comienzos de junio con grandes reservas de grasa y músculo. Durante las dos semanas siguientes, las reservas de grasa y proteína (músculos pectorales) disminuyeron, mientras que la molleja, los proventrículos, el largo del intestino, el hígado y posiblemente las gónadas aumentaron de tamaño. La mayoría de las reservas fueron usadas antes del período de puesta de huevos, por lo que éstas no estuvieron disponibles para la formación de los huevos. En algunas hembras, el desarrollo temprano de los óvulos sugiere que las reservas corporales pueden ser incorporadas en los primeros huevos. Si bien las reservas pueden ser usadas para sobrevivir en el momento de la llegada cuando las condiciones son difí ciles, su rápida pérdida y el aumento concomitante en otros órganos sugiere que una de las funciones principales de las reservas podría ser facilitar la transformación de un estado fisiológico apropiado para la migración a un estado apropiado y posiblemente requerido para la reproducción exitosa.

scholarly journals Discovery of breeding grounds of a Siberian Crane Grus leucogeranus flock that winters in Iran, via satellite telemetry

2002 ◽  
Vol 12 (4) ◽  
pp. 327-333 ◽  
Author(s):  
Yutaka Kanai ◽  
Meenakshi Nagendran ◽  
Mutsuyuki Ueta ◽  
Yuri Markin ◽  
Juhani Rinne ◽  
...  
Keyword(s):  
Satellite Telemetry ◽  
Migration Routes ◽  
Breeding Area ◽  
Volga River ◽  
Western Population ◽  
Siberian Crane ◽  
Grus Leucogeranus ◽  
Volga River Delta ◽  
Breeding Grounds ◽  
Conservation Plan

Siberian Crane Grus leucogeranus occurs only in Asia, and is Critically Endangered. The western population of the species has been almost extirpated, wintering at just two known sites, in Iran and India. To help conserve species that migrate long distances it is essential to have a comprehensive conservation plan that includes identification of migration routes and key resting areas. One Siberian Crane was satellite-tracked from the south Caspian Sea to its breeding grounds in Russia during the spring of 1996. The crane began migration on 6 March, and completed its migration on 1 May. This destination was formerly unknown as a breeding area for the species. During migration, the crane rested primarily at the eastern end of the Volga River delta. This suggests that the delta may be an important resting site for Siberian Crane.

scholarly journals Baseline and Stress‐Induced Levels of Corticosterone during Different Life Cycle Substages in a Shorebird on the High Arctic Breeding Grounds

2002 ◽  
Vol 75 (2) ◽  
pp. 200-208 ◽  
Author(s):  
Jeroen Reneerkens ◽  
R. I. Guy Morrison ◽  
Marilyn Ramenofsky ◽  
Theunis Piersma ◽  
John C. Wingfield
Keyword(s):  
Life Cycle ◽  
High Arctic ◽  
Breeding Grounds

USE OF BODY STORES IN SHOREBIRDS AFTER ARRIVAL ON HIGH-ARCTIC BREEDING GROUNDS

The Auk ◽  
2004 ◽  
Vol 121 (2) ◽  
pp. 333 ◽  
Author(s):  
R. I. Guy Morrison ◽  
Keith A. Hobson
Keyword(s):  
High Arctic ◽  
Breeding Grounds

scholarly journals Use of Body Stores in Shorebirds After Arrival on High-Arctic Breeding Grounds

The Auk ◽  
2004 ◽  
Vol 121 (2) ◽  
pp. 333-344 ◽  
Author(s):  
R. I Guy Morrison ◽  
Keith A. Hobson
Keyword(s):  
High Arctic ◽  
Climatic Conditions ◽  
Egg Laying ◽  
The Arctic ◽  
Nutrient Sources ◽  
Marine Habitats ◽  
Egg Formation ◽  
Breeding Grounds ◽  
Negative Exponential ◽  
Egg Components

Abstract Stable-isotope analyses of egg components can illuminate the contributions of endogenous and exogenous nutrients to egg formation in cases where birds move from one isotopic biome to another just before egg-laying, as in Red Knots (Calidris canutus islandica) and Ruddy Turnstones (Arenaria interpres interpres) nesting in the northeastern Canadian High Arctic. Those populations use inshore marine habitats during the winter and northward migration periods, but switch to feeding and breeding in terrestrial C-3 habitats in the Arctic. Upon the birds' arrival at Alert, Nunavut, Canada, stable carbon (δ13C) and nitrogen (δ15N) isotope values for their red blood cells were consistent with those expected from an inshore marine diet. After the birds switched to a terrestrial diet, those values decayed in a negative exponential fashion until they reached asymptotic values consistent with a local terrestrial C-3 diet. Components of eggs laid later in the season, including lipid-free yolk, albumen, yolk lipid, and albumen lipid, generally showed isotope values consistent with routing from exogenous (i.e. local) nutrient sources, which supports the notion that High Arctic shorebirds are primarily income and not capital breeders. However, eggs in earliest clutches were enriched in 13C and 15N, which suggests that some residual marine nutrients were available for reproduction. The extent to which endogenous stores are transferred to eggs in High Arctic shorebirds may depend on year and climatic conditions.

Use of Body Stores in Shorebirds after Arrival on High-Arctic Breeding Grounds

The Auk ◽  
2004 ◽  
Vol 121 (2) ◽  
pp. 333-344
Author(s):  
Keith A. Hobson
Keyword(s):  
High Arctic ◽  
Breeding Grounds

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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