Bibliographic record
Abstract
All indicators are derived from the daily bias-corrected climate data of the ISIMIP3b input datasets and are organized into two groups. The first five are directly calculated from daily mean and maximum 2 m air temperature and daily precipitation, respectively. The remaining four indicators are derived from the Canadian Forest Fire Weather Index (FWI), produced by Yi-Ling Hwong (IIASA) and kindly provided to PIK. Currently, the FWI-based indicators cover only the historical period from 1974 onwards and the baseline period for two of these FWI-based indicators is set to 1974–2004. In a future update, we plan to extend the historical time series back to 1851 and apply 1851–1900 as the baseline consistently for all indicators. All data is reduced by masking the marine areas. Detailed descriptions of all indicators are provided below, where each list item corresponds to the variable name used in the dataset. ⚫︎ temp-annual-max (TXx): Annual maximum temperature ⚫︎ temp-variability (TV): Temperature Variability ⚫︎ pr-annual-max (Rx1day): Maximum over the year of the 1-day precipitations ⚫︎ pr-extreme-99-9: Extreme Daily Precipitation Annual Sum (above 99.9th percentile of baseline period daily data) ⚫︎ pr-wetdays-gt1mm (W): Number of wet days > 1mm precipitation ⚫︎ fwi-annual-max (FWIXx): Maximum of the daily FWI over the year ⚫︎ fwi-ndays-extreme (FWIxd): Number of days with extreme fire weather above 95th percentile of 1974-2004 baseline ⚫︎ fwi-avg-seasonal (FWISA): Annual Maximum of Seasonal Average of Fire Weather Index (90 days running mean) ⚫︎ fwi-season-length (FWILS): Number of days for which the FWI is above the mean of minimum and maximum of the FWI over the reference period 1974-2004 for this single ensemble member These datasets have been created within the SPARCCLE project framwork as Deliverable D2.1 (https://sparccle.eu/).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.024 |
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".