Explaining the Non-significant Changes in Ice-off Date over Six Decades at Lake of Bays and Lake Nipissing, South-Central Ontario
Bibliographic record
Abstract
The phenomenon of non-significant trends in ice-off date under a warming climate was quantitatively explained by three efforts: exploring possible driving factors where possible and defining new factors to represent snow conditions, identifying the contributing factors through correlation and trend tests, and evaluating relative contributions through partial Mann-Kendall method. Why the ice-off became only slightly earlier over 62 years at Lake of Bays has been satisfactorily assessed: the increased winter temperature, increased total rain and decreased days of snow on ground acted as three promoting drivers to earlier ice-off date, but their promoting functions were effectively offset by adverse changes in four other factors (snowfall slope, precipitation slope, snowpack slope, and last day of snow). The ice-off date at Lake Nipissing did not have a significant trend over 58 years, although there were five factors contributing to the ice-off decline without sufficient offsetting, suggesting that the ice-off of this lake may not be sensitive, or basically elastic, to the climatic variation stressor. Relative contributions of drivers as calculated helped explain how much they contributed to ice-off trends or how much they offset the influences.
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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 | 0.000 | 0.001 |
| 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 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".