Functionality of Ice Line Latitudinal EBM Tenacity (FILLET). Protocol Version 1.1
Bibliographic record
Abstract
Abstract The Functionality of Ice Line Latitudinal EBM Tenacity (FILLET) project is a CUISINES exoplanet model intercomparison project that compares various energy balance models (EBMs) through a series of numerical experiments. The objective is to establish rigorous protocols that enable the identification of intrinsic differences among EBMs that could lead to model-dependent results for past, current, and future EBM studies. Such efforts also provide the community with an EBM ensemble average and standard deviation, rather than a single model prediction, on benchmark cases typically used by EBM studies. These experiments include Earth-like planets at different obliquity, instellation, and CO 2 abundance. Here we update the v1.0 protocol to accommodate the requirements of previously untested community models. In particular, we expand the range of CO 2 abundances for Experiment 4 to ensure any code will capture both snowball and ice-free end states. Additionally, participants are now required to report two ice edge latitudes per hemisphere to fully distinguish all climate states (snowball, ice caps, ice belts, and ice-free). The outputs described in FILLET protocol version 1.0 have also now been revised to include the maximum and minimum ice extent, in latitude, for each hemisphere, as well as the diffusion coefficient and outgoing longwave radiative flux.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.382 | 0.155 |
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".