Evaluation of multiple datasets for producing snow-cover indicators for Canada
Bibliographic record
Abstract
Snow Cover is an essential climate variable. Indicators of trends in the temporal and spatial patterns of snow cover are increasingly used to both monitoring climate variability and change and quantifying regional environmental conditions. However, the choice and accuracy of the indicators are often determined by the input snow cover data. A survey of snow cover indicators is performed to identify both those that would satisfy user requirements over Canada in addition to the current practices for international reporting. Four different input data sets are then used to generate snow cover indicators over a five year period (2006-2010): the Canadian Meteorological Centre snow depth analysis with systematic in-situ measurements; cloud free MODIS MOD10C1 snow cover product; NOAA Interactive Mapping Service 4km snow cover product; and CCRS/CMC snow cover product that assimilated both CMC inputs and NOAA AVHRR satellite imagery. Then snow cover indicators, including snow cover onset and melt are evaluated through their sensitivity to documented data uncertainties, by comparison to continuous monitored in-situ sites, and through inter-comparison. Recommendations for suitable indicators as a function of input dataset are provided.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".