Development of moderate-resolution gridded monthly air temperature and degree-day maps for the Labrador-Ungava region of northern Canada
Bibliographic record
Abstract
Detailed climatological grids are needed for many applications, including permafrost prediction, ecological modelling and infrastructure planning. This study describes the creation of moderate-resolution gridded climate datasets covering the entire Labrador-Ungava region (50°–63°N) for a series of climate indices, including monthly air temperature, annual air temperature, freezing degree-days (FDDs) and thawing degree-days. Using a recently developed spatiotemporal infilling technique, temporally consistent climate grids spanning the 1948–2014 period were derived at a monthly resolution. Comparison against within-sample and out-of-sample climate stations revealed thin plate spline smoothing as more accurate for modelling air temperatures than regression, kriging and co-kriging. Evaluation of derived air temperature grids across a wide range of environments and scenarios shows an overall accuracy of 0.8 ± 0.3 °C. Spatially distributed air temperatures were converted to thawing and FDDs using an empirical transfer function that compensates for the impacts of continentality and coastal proximity. These climate datasets will form the basis of inputs for future ecological and environmental modelling in the eastern Labrador-Ungava region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".