Sea Ice Rheology Experiment (SIREx) - Model output data
Bibliographic record
Abstract
Sea-ice model output analyzed in the Sea Ice Rheology Experiment (SIREx) Part I and Part II. There is one netCDF file per model, per year (1997 and/or 2008). Each netCDF file contains daily output (as means or instantaneous values -- as indicated in the name) for January-February-March of the given year. Please see below for more information on what is included and how to cite. 1. Naming convention of the files "Model simulation label" + _ + "year" + _ + "daily_means" OR "instant" 2. Variables included U,V : sea-ice velocity in the x,y directions (in m/s); A: ice concentration per grid cell (0 -- 1); h: mean ice thickness per grid cell (in m); Grid spacing information: centered on u-points (DXU, DYU), v-points (DXV, DYV), or t-points (DXT, DYT) (in m); Grid point coordinates: for u-points (ULON, ULAT), v-points (VLON, VLAT), or t-points (TLON, TLAT) (in degrees); maskT: land mask at t-points (0/1 = ocean/land); time: time axis (in days since 1901-01-01 00:00:00). 3. Recommended citation usage If all simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx papers (Bouchat et al., 2022, Hutter et al., 2022 ). If only selected simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".