Bibliographic record
Abstract
Monthly climate data of average, minimum, and maximum temperature and precipitation normalized for the period 1961 through 1990 were accumulated from approximately 3,000 weather stations in the Western United States and Southwestern Canada. About two-thirds of these observations were available from the weather services of the two countries while the remaining third were added to the normalized base from daily weather records of stations of short duration. Tests of the procedures used to normalize these supplemental data showed that estimates on average were within 0.2 oC for temperature variables and 2.7 mm for precipitation.Weather data for the 48 monthlies were fit to geographic surfaces with thin plate splines. Relationships between predicted values and observed monthlies for about 245 records withheld from the modeling process produced values of R2 that averaged about 0.95 and ranged from 0.87 to 0.99. The slope of the regression line for these relationships was essentially 1.0 for all 48 comparisons. Predictions from the climate model can then be converted to variables of demonstrated importance in plant geography, ecology, or physiology. As an illustration, algorithms are presented and justified for estimating 18 variables derived from predicted values. These derived variables range from the straightforward such as mean annual temperature or mean temperature in the coldest month to those of degree-days >5 oC or freezing dates. Applications of the model in plant biology are illustrated for (1) generating climate estimates for locations specified by latitude, longitude, and elevation, (2) mapping climate variables, (3) separating species distributions in climatic space, and (4) relating genetic variation among populations to climatic gradients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".