Heating degree day spatial datasets for Canada
Bibliographic record
Abstract
Heating degree days (HDD) represent a concise measure of heating energy requirements used to inform decision making about the impact of climate change on heating energy demand. This data paper presents spatial datasets of heating degree days (HDD) for Canada for two thirty-year periods, 1951-1980 and 1981-2010, using daily temperature gauge observations over these time periods. Stations with fewer than nine missing days in a year and greater than nine years of data over each thirty-year period were included, resulting in 1339 and 1679 stations for the 1951-1980 and 1981-2010 periods respectively. Mean absolute error (MAE) of the spatial models ranged from 124.2 Celsius degree days (C-days) for the 1951-1980 model (2.4% of the surface mean) to 137.6 C-days for the 1981-2010 model (2.7%). This note presents maps illustrating cross validation errors at a set of representative stations. The grids are available at ∼2 km resolutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".