Empirical models for forecasting changes in the phenology of ice cover for Canadian lakes
Bibliographic record
Abstract
In situ and remote-sensed data on freeze-up and break-up dates for lakes spread over much of Canada were used to develop and validate simple regression models linking lake ice phenology to climatic conditions and lake morphometry. The primary variables affecting fall freeze-up dates were the fall date when 30-day smoothed air temperatures reached 0 °C and lake mean depth; the primary variables affecting spring break-up date were the spring date when 30-day smoothed air temperatures reached 0 °C, solar elevation on that date, and the number of days over winter when 30-day smoothed air temperatures were <0 °C. These models were used to project potential impacts of climate change on ice phenology across Canada; by 2055 (under the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) A2 emissions scenario), freeze-up dates were projected to be an average of 10 days later. Break-up dates were projected to be from 0 to 16 days earlier, with greater changes occurring at higher latitudes. These projections were similar to those independently derived using a mechanistic ice phenology model.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".