Spatial climate models for Canada’s forestry community
Bibliographic record
Abstract
We summarize ongoing efforts at the Canadian Forest Service to produce spatial climate models for Canada and the United States. Our models, which encompass a wide range of variables and spatiotemporal extents, typically employ thin plate smoothing splines to interpolate and extrapolate climate station values as a function of latitude, longitude and elevation. The resulting surfaces can be resolved as grids (i.e., maps) or as point estimates at locations of interest. Recent efforts, detailed here include: updated models for the most recent 30-year normal period (i.e., 1981–2010), moisture balance models, future climate projections using the latest round of general circulation model (GCM) outputs and emissions scenarios, lake ice freeze/thaw models, and growing season models. These models are available to the Canadian forest community and beyond via the internet ( http://cfs.nrcan.gc.ca/projects/3 ) or by contacting the senior author.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".