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NUMERICAL SIMULATION OF ARCTIC HALOCLINE FORMATION

2020· article· en· W3100522052 on OpenAlexaboutno aff
Marina A. Tarkhanova, Elena Golubeva

Bibliographic record

VenueInterexpo GEO-Siberia · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHaloclineStratification (seeds)ArcticThermohaline circulationCanada BasinClimatologyOceanographyGeologyThe arcticStructural basinEnvironmental scienceSalinityGeomorphology

Abstract

fetched live from OpenAlex

This paper discusses issues related to the analysis of the Arctic halocline state over the past decades. Observational data show that the layer of halocline in the Arctic Ocean significantly changed in the last 40 years, which may affect the Arctic ice cover. For the study we used a three-dimensional ocean and sea ice numerical model developed at the ICMMG SB RAS. The main attention was devoted to the analysis of the model distribution of water salinity in the upper 250-meter layer and its variability. Based on numerical experiments on the sensitivity of thermohaline stratification to variations in atmospheric effects and the intensity of river flow, we identified areas of the Arctic basin in which the variability of the Arctic halocline was the most pronounced.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.235
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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