MétaCan
Menu
← Back to cohort
Record W4238007288 · doi:10.1002/essoar.10507260.1

Surface salinity under transitioning ice cover in the Canada Basin: Climate model biases linked to vertical 2 distribution of freshwater

2021· preprint· en· W4238007288 on OpenAlexaffabout
Erica Rosenblum, Robert Fajber, Julienne Strœve, Sarah T. Gille, Bruno Tremblay, Eddy C. Carmack

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsFisheries and Oceans CanadaMcGill UniversityUniversity of Manitoba
Fundersnot available
KeywordsSalinityDistribution (mathematics)World Wide WebGeologyComputer scienceOceanography

Abstract

fetched live from OpenAlex

The Canada Basin has exhibited a significant trend toward a fresher surface layer and thus a more stratified upper ocean over the past three decades. Here, we explore the extent to which the Community Earth System Model (CESM) accurately simulates the observed surface freshening and seasonal processes that contribute to the freshening. We examine 30 simulations from CESM1 (used in the IPCC AR5), 3 simulations from CESM2 (IPCC AR6), and ocean observations from 1975 and 2006-2012. In contrast to the observations, the models simulate salinity profiles that show relatively little variation between 1975 and 2012. We demonstrate that this bias can be partly attributed to the model’s tendency to mix freshwater too deep, creating a surface layer that is saltier than observed. The results provide insight for climate model improvement that could have wide-reaching implications because upper-ocean stratification influences the vertical transport of heat and nutrients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.037
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.239
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicArctic and Antarctic ice dynamics→French-language works237,207→