Northern Hemisphere historical in-situ Snow Water Equivalent dataset (1979-2021)
Bibliographic record
Abstract
Description (in English, French follows) The Northern Hemisphere historical in situ snow water equivalent (SWE) dataset includes observations from manual snow surveys and airborne passive gamma radiation SWE estimates for the period 1979-2021 compiled from eight different sources covering North America, Russia and Finland. The dataset was compiled to support evaluation of gridded SWE products over the modern satellite era (1979-2021). The data are distributed as a single compressed NetCDF file (NH_insitu_swe_dataset_19792021_v1.zip). More details about the dataset are given in the ReadMe_NHinsituSWE_v1.pdf. Description (Francais) La base de données historiques de mesures in situ de l’Équivalent en Eau de la Neige (EEN) de l’Hémisphère Nord comprend des observations manuelles issues de lignes de neige et des estimations de l’EEN provenant de mesures de radiation gamma aéroportées pour la période 1979-2021. Cette base de données compile des données issues de huit sources couvrant l’Amérique du Nord, la Russie et la Finlande. La base de données a été développée pour servir à l’évaluation de produits d’EEN sur grille couvrant la période moderne de la télédétection satellitaire (1979-2021). Les données sont distribuées au format NetCDF (NH_insitu_swe_dataset_19792021_v1.zip). Des informations complémentaires sur cette base de données sont fournies dans le fichier LisezMoi_EENinsituHN_19792021_v1.pdf.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.035 |
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".