Bibliographic record
Abstract
Announcements Deprecation: Release 0.25.0 of xclim will be the last version to explicitly support Python3.6 and xarray<0.17.0. New indicators land.winter_storm computes days with snow accumulation over threshold. land.blowing_snow computes days with both snow accumulation over last days and high wind speeds. land.snow_melt_we_max computes the maximum snow melt over n days, and land.melt_and_precip_max the maximum combined snow melt and precipitation. snd_max_doy returns the day of the year where snow depth reaches its maximum value. atmos.high_precip_low_temp returns days with freezing rain conditions (low temperature and precipitations). land.snow_cover_duration computes the number of days snow depth exceeds some minimal threshold. land.continuous_snow_cover_start and land.continuous_snow_cover_end identify the day of the year when snow depth crosses a threshold for a given period of time. days_with_snow, counts days with snow between low and high thresholds, e.g. days with high amount of snow (indice and indicator available). fire_season, creates a fire season mask from temperature and, optionally, snow depth time-series. New features and enhancements generic.count_domain counts values within low and high thresholds. run_length.season returns a dataset storing the start, end and length of a season. Fire Weather indices now support dask-backed data. Objects from the xclim.sdba submodule can be created from their string repr or from the dataset they created. Fire Weather Index submodule replicates the R code of cffdrs, including fire season determination and overwintering of the drought_code. New run_bounds and keep_longest_run utilities in xclim.indices.run_length. New bias-adjustment method: PrincipalComponent (based on Hnilica et al. 2017 https://doi.org/10.1002/joc.4890). Internal changes Small changes in the output of indices.run_length.rle.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.227 | 0.308 |
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".