Updated plant hardiness zones for Canada and assessment of change over time
Bibliographic record
Abstract
Plant hardiness systems have been developed for various regions around the world to help ensure that cultivated plants are grown at locations where suitable climate conditions prevail. In Canada, a multivariate plant hardiness index was developed in the 1960s that incorporates several temperature- and precipitation-related variables, as well as snow depth and wind speed. In the United States, the plant hardiness system involves averaging annual extreme minimum temperatures over a period of interest, with values subsequently classified into hardiness zones. Here we report on efforts to update hardiness zone maps for Canada using both the Canadian and US approaches and using climate data for the 1991-2020 period. The two hardiness systems produced generally similar spatial patterns in plant hardiness across Canada, including high index values in southern and coastal regions and low index values in northern and high-elevation areas. Detailed comparisons to previous hardiness maps indicated that, since 1961-1990, zone values have increased by between half a zone and two full zones across the country, with the largest increases occurring in western and northwestern Canada. For the multivariate Canadian hardiness system, a change attribution analysis indicated that three temperature-related variables were primarily responsible for driving the observed changes in the plant hardiness zones. The new maps are available at http://planthardiness.gc.ca .
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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 | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".