The generation of USDA-equivalent extreme minimum temperature models and a comparison with Canada’s plant hardiness zones
Bibliographic record
Abstract
Hardiness zones are widely used in North America to support the trade of plants and recommendations on local use of perennial plant species. In Canada, two zonation approaches are in use, a made-in-Canada model that integrates seven climate variables and the United States Department of Agriculture’s (USDA) extreme minimum temperature map/model. In this paper we develop and present several extreme minimum temperature models for the 1961–1990 and 1971–2000 climate normal periods and annual models for the winter seasons of 1961 through 2000. These models are similar in nature to the USDA plant hardiness model/map. We compare these models with a recent update of the Canadian plant hardiness zones developed with the same mathematical interpolation techniques (thin plate smoothing splines). Individual Canadian zones typically span five to nine USDA equivalent sub-zones in total, although most of the area (>75%) of each zone generally spans 3–4 USDA sub-zones. We note that there is no simple transformation of one zonation approach to the other, but values for both systems can now be obtained for any location in Canada using an internet mapping tool. Over the period of 1961–2000 extreme minimum temperature has been trending upward in most provinces. These trends may be affecting plants in some locations, but a lack of survival and/or hardiness data is a significant impediment to assessing this. Variation in extreme minimum temperature over the 1961 to 2000 period was greatest in western North America, particularly interior British Columbia. Key words: Climate mapping and trends, spatial analysis, ANUSPLIN
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".