Spatial datasets of 30-year (1991–2020) average monthly total precipitation and minimum/maximum temperature for Canada and the United States
Bibliographic record
Abstract
Thin plate smoothing spline models, covering Canada and the continental United States, were developed using ANUSPLIN for 30-year (1991-2020) monthly mean maximum and minimum temperature and precipitation. These models employed monthly weather station values from the North American dataset published by National Oceanic and Atmospheric Administration's (NOAA's) National Centers for Environmental Information (NCEI). Maximum temperature mean absolute errors (MAEs) ranged between 0.54 °C and 0.64 °C (approaching measurement error), while minimum temperature MAEs were slightly higher, varying from 0.87 °C to 1.0 °C. On average, thirty-year precipitation estimates were accurate to within approximately 10 % of total precipitation levels, ranging from 9.0 % in the summer to 12.2 % in the winter. Error rates were higher in Canada compared to estimates in the United States, consistent with a less dense station network in Canada relative to the United States. Precipitation estimates in Canada exhibited MAEs representing 14.7 % of mean total precipitation compared to 9.7 % in the United States. The datasets exhibited minimal bias overall; 0.004 °C for maximum temperature, 0.01 °C for minimum temperature, and 0.5 % for precipitation. Winter months showed a greater dry bias (0.8 % of total winter precipitation) compared to other seasons (-0.4 % of precipitation). These 30-year gridded datasets are available at ∼2 km resolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".