Bibliographic record
Abstract
CloudMicrophysics v0.16.0 Diff since v0.15.2 Merged pull requests: homogeneous freezing to parcel (#267) (@amylu00) lat heat of fus in parcel dT/dt and dSl/dt (#282) (@amylu00) water based deposition nucleation (#292) (@amylu00) Move P3 mass functions to src, add gamma functions to docs (#293) (@trontrytel) F_r = 0 exception fixed, mass tests updated (#298) (@anastasia-popova) Add shape parameters solver for P3 (#299) (@trontrytel) Cleanup in the landing page and docs (#302) (@trontrytel) unitless N_hat added, Float32 tests passing (#303) (@anastasia-popova) replacing Mohler AF with nucleation rate (#304) (@amylu00) add notice (#309) (@trontrytel) fix link (#311) (@trontrytel) dont show plot examples code in docs (#312) (@trontrytel) correct naming for abifm desert dust params (#314) (@amylu00) Refactor parameters to use ClimaParameters API (#315) (@nefrathenrici) Update Alpha_va and Gamma Functions (#317) (@anastasia-popova) Update to CLIMAParameters v0.9 and Thermodynamics v0.12 (#320) (@trontrytel) Closed issues: Update the github landing page (#152) Integrate homogeneous freezing in the parcel model (#183) check the sign in the aspect ratio power in terminal velocity (#220) Compute lambda and N_0 based on m(D), N_tot and q (#227) Gamma distribution in parcel missing factor of 1/3 (#276) Add gamma distribution option for deposition growth (#277) Use droplets for immersion freezing in parcel (#278) Update to the new CLIMAParameters (#286) Allow for zero rimed mass and volume (#289) Add documentation about shape parameters solve in P3 (#294) Add liquid - ice phase change to parcel model equations (#295) Add homogeneous freezing to parcel (#296) Add water activity based dust deposition parameterization (#297) Switch dust deposition parameterization to compute the rate (#307)
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.324 | 0.310 |
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".