A refined zirconium-in-rutile thermometer

2020 ◽  
Vol 105 (6) ◽  
pp. 963-971 ◽  
Author(s):  
Matthew J. Kohn
Keyword(s):  
Experimental Data ◽  
Stability Field ◽  
Careful Attention ◽  
Data Set ◽  
Natural Logarithm ◽  
Poor Understanding ◽  
Natural Data ◽  
Compositional Variability ◽  
Zr In Rutile ◽  
Combined Data

Abstract The zirconium-in-rutile thermometer enjoys widespread use, but confidence in its accuracy is limited because experiments were conducted at higher temperatures than many rutile-bearing rocks and calibration uncertainties have not been quantitatively assessed. Refined calibrations were developed using bootstrap regression to minimize residuals in the natural logarithm of the equilibrium constant, based on experiments only (n = 32) and on a combined compilation of experiments and natural data (n = 94, total). Rearranging the regression to solve for T, and expressing Zr concentration (C) in parts per million (μg/g), the calibrations in the α-quartz stability field are: Experimental data set: T ( C ∘ ) = 68740 + 0 . 441 · P ( bars ) - 0 . 114 · C ( ppm ) 129 . 76 - R · ln [ C ( ppm ) ] - 273 . 15 . Combined data set: T ( C ∘ ) = 71360 + 0 . 378 · P ( bars ) - 0 . 130 · C ( ppm ) 130 . 66 - R · ln [ C ( ppm ) ] - 273 . 1 . Thermodynamics of the quartz-coesite transition as applied to the calibration for α-quartz yields calibrations for the coesite stability field: Experimental data set T ( C ∘ ) = 71290 + 0 . 310 · P (  bars  ) - 0 . 114 · C ( ppm ) 128 . 76 - R · ln [ C ( ppm ) ] - 273 . 15 . Combined data set: T ( C ∘ ) = 73910 + 0 . 247 · P ( bars ) - 0 . 130 · C ( ppm ) 129 . 65 - R · ln [ C ( ppm ) ] - 273 . 15. Propagated temperature uncertainties are ±20–30 °C (2σ) for the experimental data set calibration, and ±10–15 °C (2σ) for the combined data set. Compared to previous experimental calibrations, the refined thermometer predicts temperatures up to 40 °C lower for T ≤ 550 °C, and systematically higher temperatures for T > 800 °C. With careful attention to distributions of Zr in rutile grains, precisions of ±5 °C and accuracies ~±15 °C may be possible, although a poor understanding of how to select compositions for thermometry will typically lead to larger uncertainties. The ZiR calibration promises continued high-precision and accurate thermometry, and possibly improved thermodynamic properties, but the sources of compositional variability in rutile warrant further scrutiny.

EXPERIMENTAL DATA SET NO. 24: PHASE DISTRIBUTION IN BUBBLY FLOW

1992 ◽  
Vol 6 (1-4) ◽  
pp. 257-301 ◽  
Author(s):  
Akimi Serizawa ◽  
Isao Kataoka ◽  
Itaru Michiyoshi
Keyword(s):  
Experimental Data ◽  
Phase Distribution ◽  
Bubbly Flow ◽  
Data Set

Selection of children with ultra-severe traumatic brain injury for neurosurgical intervention

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decision Tree ◽  
Prediction Model ◽  
Severe Traumatic Brain Injury ◽  
Pupillary Response ◽  
Data Set ◽  
Robust Model ◽  
Gcs Score ◽  
Combined Data

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.

Subcratonic and tectonic evolution of pyroxenite and eclogite with lamellar inclusions in garnet, Western Gneiss Region, Norway

2021 ◽  
Author(s):  
Dirk Spengler ◽  
Taisia A Alifirova ◽  
Herman L M van Roermund
Keyword(s):  
Stability Field ◽  
Mantle Lithosphere ◽  
Western Gneiss Region ◽  
Data Set ◽  
Entire Area ◽  
Ultra High Pressure ◽  
Planar Structures ◽  
Lamellar Inclusions ◽  
Al2o3 Concentration ◽  
Orogenic Peridotite

Abstract Oriented lamellar inclusions of pyroxene and rutile in mantle garnet often serve as evidence for majoritic and titaniferous precursor garnets, respectively. We investigated ten new such microstructure-bearing samples from six orogenic peridotite bodies in SW Norway, which originated in the E Greenland mantle lithosphere, petrologically and thermobarometrically. All pyroxenite (nine) and eclogite (one) samples have large (mainly porphyroclastic) garnet containing silicate and oxide inclusions with shape-preferred orientation relationship. These inclusions vary – dependent on their size – systematically in shape (acicular to subprismatic), width (∼50 μm to submicron size), spacing (several 100 to ∼10 μm) and phase (pyroxene to Ti-oxide ± pyroxene). Smaller inclusions can fill the space between larger inclusions, which support the idea of consecutive generations. The larger, early formed lamellae occur least frequent and are most poorly preserved. A younger generation of other inclusions decorates healed cracks cutting across cores but not rims of garnet. These inclusions comprise oxides, silicates, carbonates (aragonite, calcite, magnesite) and fluid components (N2, CO2, H2O). The older, homogeneously distributed inclusions comply texturally and stoichiometrically with an origin by exsolution from excess Si- and Ti-bearing garnet. Their microstructural systematic variation demonstrates a similar early evolution of pyroxenite and eclogite. The younger inclusions in planar structures are ascribed to a metasomatic environment that affected the subcratonic lithosphere. The microstructure-bearing garnets equilibrated at ∼3.7 GPa (840 °C) and ∼3.0 GPa (710 °C), at a cratonic geotherm related to 37–38 mW m−2 surface heat flow. Some associated porphyroclastic grains of Mg-rich pyroxene have exsolution lamellae of Ca-rich pyroxene and vice versa that indicate a preceding cooling event. Projected isobaric cooling paths intersect isopleths for excess Si in garnet at ∼1550 °C, if an internally consistent thermodynamic data set in the system Na2O–CaO–MgO–Al2O3–SiO2 (NCMAS) is applied (or ∼1600 °C if using CMAS). This temperature may confine the crystallisation of the unexsolved garnets at 100–120 km depths of the E Greenland subcratonic lithosphere. Tectonism is indicated in coastal and hinterland samples by porphyroclastic orthopyroxene with Al2O3 concentrations showing W-shaped profiles. Cores of associated large (&gt;200 μm) recrystallised grains have low Al2O3 contents (0.18–0.23 wt.%). Both characteristics typify relatively short intracrystalline Al diffusion lengths and a prograde metamorphism into the diamond stability field. We assign this event to subduction during the Scandian orogeny. Porphyroclastic orthopyroxene in other samples shows U-shaped Al2O3 concentration profiles paired with long Al diffusion lengths (several 100 μm) that exceed the radius of recrystallised grains. Their cores contain high Al2O3 contents (0.65–1.16 wt.%), consistent with a diffusional overprint that obliterated prograde and peak metamorphic records. Unlike Al2O3, the CaO content in porphyroclastic orthopyroxene cores is uniform suggesting that early exhumation was subparallel to Ca isopleths in pressure–temperature space. The depth of sample origin implies that rock bodies of Scandian ultra-high pressure metamorphism occur in nearly the entire area between Nordfjord and Storfjord and from the coast towards ∼100 km in the hinterland, i.e. in a region much larger than anticipated from crustal eclogite.

scholarly journals Coreset Clustering on Small Quantum Computers

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1690
Author(s):  
Teague Tomesh ◽  
Pranav Gokhale ◽  
Eric R. Anschuetz ◽  
Frederic T. Chong
Keyword(s):  
Quantum Algorithms ◽  
Quantum Computers ◽  
Data Sets ◽  
New Paradigm ◽  
Data Set ◽  
Small Quantum ◽  
Natural Data ◽  
Near Term ◽  
Classical Computing ◽  
Quantum Speedup

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.

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