Canada’s plant hardiness zones revisited using modern climate interpolation techniques
Bibliographic record
Abstract
Canada’s plant hardiness zones are well known to Canadian gardeners. The original hardiness indices and zones were developed in the early 1960s through regression models of several climatic parameters and plant survival data from numerous locations across the country. Since that time Canada’s climate has changed and climate interpolation techniques have improved. We have remapped Canada’s plant hardiness zones using data from the period of the original analysis (roughly 1930–1960) and for the 1961–1990 period using thin plate spline interpolation methods. Trials of bivariate and tri-variate splines were undertaken and evaluated using withheld data. A trivariate function of position (longitude and latitude) and elevation performed best. Standard errors of the surfaces were about 0.5°C or less for temperature variables and 5 to 28% for rainfall depending on the month (winter months being the worst). The creation of a new digital elevation model (a regular grid of position and elevation) of Canada enabled the mapping of each variable required for the plant hardiness formula at spatial resolutions of 1 km to 10 km. These models better capture the spatial variation in climate than previously possible and hence should provide a stronger basis for applications such as the determination of plant hardiness zones. Comparisons of the zones between the two time periods are consistent with what is known about climate in Canada. The hardiness index has declined or has stayed stable in eastern Canada and has increased in western areas. The results also suggest that more station data are required in western Canada to better capture the inherent spatial variability of climate, particularly precipitation, in mountainous terrain. Key words: Plant hardiness, thin plate splines, climate mapping, spatial analysis
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".