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Record W6968661198 · doi:10.5281/zenodo.6314188

CICE-Consortium/CICE: CICE Version 6.3.1

2022· other· en· W6968661198 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsDebuggingDocumentationInterface (matter)Scripting languageCode refactoringForcing (mathematics)Benchmark (surveying)Window (computing)Modelica

Abstract

fetched live from OpenAlex

CICE6.3.1 is a minor update of CICE6.3.0 from September, 2021. It is released with Icepack1.3.1. This update includes several minor changes including a bug fix that led to errors in multi-processor runs with advection="none". This version also updates a few aspects of the dynamics implementation, upgrades the OpenMP performance, updates the atmbndy options associated with a change in Icepack1.3.1, and updates several other technical features. Major Changes None Bug fixes Fix multi-pe advection bug setting, advection=none was not working on multiple pes. #664 Enhancements Implementation of plastic potential for VP and EVP. #649 Refactor EVP and VP, express rheology term as a function of viscous coefficients, update documentation #639, #647. Note: This changes answers at roundoff level. Update calls to seabed stress to improve efficiency #673 Deprecate gx1 CORE forcing option #643 Update OpenMP implementation to debug and improve performance. #680, #693. Note: May require setting "setenv OMP_STACKSIZE 64M" or similar in machine env file Update PIO fill value on restart #675 Update namelist reading to allow namelist groups to be read in any order #671, #677 Update Makefile to improve dependency logic #667 Update Icepack #658 #691 Note: changes results when formdrag is applied and fbot_xfer_type == 'Cdn_ocn' Update testing scripts #644, #658, #665, #664, #678 Update NUOPC/CMEPS coupling interface to add new snow features and more #668, #670 Update DMI coupling interface to add new snow features #641 Refactor/reduce calls to calc_ffrac in eap solver #638 Update atmbndy input to support similarity (aka default), constant, and mixed options, update documentation #633. Zero out eap variables on land consistently #632 Update history implementation allocation checks #631 Port to Narwhal #678 Documentation Update version and copyright date #691 Update documentation #644, #652, #657 Update README.md to reflect change of default branch name from master to main in Github repository.

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)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.211
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0060.006
Open science0.0050.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.2110.252

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 head0.027
GPT teacher head0.248
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreSoftware

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
Published2022
Admission routes1
Has abstractyes

Explore more

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