Supplementary material to "Multivariate bias corrections of climate simulations: Which benefits for which losses?"
Bibliographic record
Abstract
Spatial-MRec Full-MRec 0.0 0.5 1.0 1.5 2.0 2.5 Mean difference (T2) (a) Model CDF-t R2D2 2d-dOTC Spatial-dOTC Full-dOTC MBCn 2d-MRec Spatial-MRec Full-MRec 0.0 0.2 0.4 0.6 0.8 Mean relative difference (PR) (b) Model CDF-t R2D2 2d-dOTC Spatial-dOTC Full-dOTC MBCn 2d-MRec Spatial-MRec Full-MRec -0.4 -0.3 -0.2 -0.1 0.0 Standard dev.relative difference (T2) (c) Model CDF-t R2D2 2d-dOTC Spatial-dOTC Full-dOTC MBCn 2d-MRec Spatial-MRec Full-MRec -0.2 0.0 0.2 0.4 Standard dev.relative difference (PR) (d) Figure S1.Boxplots of mean (a and b) and standard deviation (c and d) differences for Temperature (T2, a and c) and Precipitation (PR, b and d) during winter over the 1979-2016 period for the Brittany region (SAFRAN reference).Results are shown for: plain IPSL; CDF-t; R 2 D 2 ; dOTC (2d-, Spatial-and Full-versions); MBC-n and MRec (2d-, Spatial-and Full-versions) outputs.Red asterisks indicate values lying outside the plotted range.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.703 | 0.205 |
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".