Development of scale‐free climate data for Western Canada for use in resource management
Bibliographic record
Abstract
Abstract Applying climate data in resource management requires matching the spatial scale of the climate and resource databases. Interpolating climate data in mountainous regions is difficult. In this study, we present methodology to generate scale‐free climate data through the combination of interpolation techniques and elevation adjustments. We apply it to monthly temperature and precipitation normals for 1961–90 produced by the Parameter‐elevation Regressions on Independent Slopes Model (PRISM) for British Columbia, Yukon Territories, the Alaska Panhandle, and parts of Alberta and the United States. Equations were developed to calculate biologically relevant climate variables including various degree‐days, number of frost‐free days, frost‐free period, and snowfall from monthly temperature and precipitation data. Estimates of climate variables were validated using an independent dataset from weather stations that were not included in the development of the model. Weather station records generally agreed well with estimated climate variables and showed significant improvements over original PRISM climate data. A stand‐alone MS Windows application was developed to perform all calculations and to integrate future climate predictions from various global circulation models. We demonstrate the use of this application by showing how climate change may affect lodgepole pine seed planning zones for reforestation in British Columbia. Copyright © 2006 Royal Meteorological Society.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".