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Record W6949653576 · doi:10.5281/zenodo.2743413

Animated E3SM V1 High Resolution Labrador Sea Ice Thickness and Concentration with mid-20th Century Atmospheric Constituents

2019· other· en· W6949653576 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSea iceSea ice concentrationHigh resolutionBackscatter (email)Sea ice thicknessIce shelfArctic ice pack

Abstract

fetched live from OpenAlex

This animated GIF file visualizes daily grid-cell mean sea ice thickness and concentration for the Labrador Sea region from version 1 of the Energy Exascale Earth System Model (E3SM) using 25km Atmosphere/Land and 8-16km Sea Ice-Ocean resolutions as described in the manuscript "The DOE E3SM coupled model version 1: Description 1 and results at high resolution". River routing is resolved at 0.125˚. This fully coupled simulation was spun-up for 6 years, and then proceeded for 50 subsequent years using HighResMIP protocols for continuuous 1950s atmospheric constituents. This animation shows sea thickness evolution in each frame for five model years 46 to 50, inclusive, with rendered transparency determined from sea ice concentration to demonstrate the influence of ocean eddies around the southern tip of Greenland. The coastline is the true model boundary. The animation is best viewed using a web browser.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0780.010

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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
GenreOther

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

Citations1
Published2019
Admission routes1
Has abstractyes

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