MITgcm configuration and simulations for the test of the teardrop yield curve
Bibliographic record
Abstract
In this archive, there is: Two versions of MITgcm checkpoint 67z (https://doi.org/10.5281/zenodo.4968496) with modified sea ice packages. The changes are limited to the files "seaice_calc_viscosities.F": The first version with the teardrop and parabolic lens yield curve as described in Zhang and Rothrock (2005) (DOI:10.1029/2004JC002599). It is present in the file "MITgcm/seaice_calc_viscosities_original.F" and copied in "build_ori" folder in "sims" The second with new formulations of teardrop and parabolic lens yield curves (These are slightly modified from the one already present in MITgcm checkpoint 67z). The changes are in the file "MITgcm/pkg/seaice/seaice_calc_viscosities.F" and linked to the folder "build_fix" in "sims". A python "yield_curve.py" script used to compare both versions of the formulation with simulated deformation data. The configuration files for the uniaxial compression experiments that were used to compare both versions of MITgcm above. ("MITgcm/exps/" and all the "data*" and "gendata.py" files in the experiments folders) The results of the simulations performed with the versions above (folders "sims/run??") run11: new formulation of the teardrop yield curve - 5h - 10 outer loops run08l2: new formulation of the teardrop yield curve - 10 timesteps - 15000 outer loops run09: original formulation of the teardrop yield curve - 5h - 10 outer loops run09l2: original formulation of the teardrop yield curve - 10 timesteps - 15000 outer loops run10: elliptical yield curve - 5h - 10 outer loops run10l : elliptical yield curve - 10 timesteps - 15000 outer loops The scripts to plot and analyze the result, used for publication. plot_yc_comp.py to plot the results of simulations plot_data.py are utilities for plot_yc_comp.py plotresgmres.py is used to plot the numerical convergence of the simulations
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".