Optimizing Input Data for Gridding Climate Normals for Canada
Bibliographic record
Abstract
Abstract Spatial models of 1971–2000 monthly climate normals for daily maximum and minimum temperature and total precipitation are required for many applications. The World Meteorological Organization’s recommended standard for the calculation of a normal value is a complete 30-yr record with a minimal amount of missing data. Only 650 stations (~16%) in Canada meet this criterion for the period 1971–2000. Thin-plate smoothing-spline analyses, as implemented by the Australian National University Splines (ANUSPLIN) package, are used to assess the utility of differing amounts of station data in estimating nationwide monthly climate normals. The data include 1) only those stations (1169) with 20 or more years of data, 2) all stations (3835) with 5 or more years of data in at least one month, and 3) as in case 2 but with data adjusted through the most statistically significant linear-regression relationship with a nearby long-term station to 20 or more years (3983 stations). Withheld-station tests indicate that the regression-adjusted normals as in dataset 3 generally yield the best results for all three climatological elements, but the unadjusted normals as in dataset 2 are competitive with the adjusted normals in spring and autumn, reflecting the known longer spatial correlation scales in these seasons. The summary mean absolute differences between the ANUSPLIN estimates and the observations at 48 spatially representative withheld stations for dataset 3 are 0.36°C, 0.66°C, and 4.7 mm, respectively, for maximum temperature, minimum temperature, and precipitation. These are respectively 18%, 7%, and 18% smaller than the summary mean absolute differences for the long-term normals in dataset 1.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".