Factors influencing peak summer surface water temperature in Canada’s large lakes
Bibliographic record
Abstract
Seasonal water temperature data from 388 large Canadian lakes (area ≥ 100 km2) were used to develop improved empirical tools for forecasting the impacts of climate change on the magnitude (TP) and time of occurrence (JP) of annual peak surface water temperatures. Analyses of remotely sensed open-water temperatures with sinusoidal models produced estimates of TP and JP predominately better than other models. Those estimates were analyzed for lake and climate patterns. Linear mixed effects regression produced a significant model of TP with fixed positive effects for mean July and annual air temperatures and lake perimeter, but negative effects with mean July and annual percent cloud cover, mean annual precipitation, range of monthly mean global clear sky radiation, area, and elevation. Subsets of the estimates with mean, maximum, or Secchi depth values produced similarly significant models with negative depth coefficients. JP was relatively invariant but small, significant lake and climate effects were detected. The best models identified in our analyses will be useful tools for forecasting how climate change will alter aspects of the limnetic seasonal water temperature cycle that strongly influences the species composition and productivity of their fisheries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".