Spatial analysis of ice phenology trends across the Laurentian Great Lakes region during a recent warming period
Bibliographic record
Abstract
We examined spatial patterns of trends in ice phenology and duration for 65 waterbodies across the Great Lakes region (Minnesota, Wisconsin, Michigan, Ontario, and New York) during a recent period of rapid climate warming (1975‐2004). Average rates of change in freeze (3.3 d decade−1) and breakup (22.1 d decade−1) dates were 5.8 and 3.3 times more rapid, respectively, than historical rates (1846‐1995) for Northern Hemisphere waterbodies. Average ice duration decreased by 5.3 d decade−1. Over the same time period, average fall through spring temperatures in this region increased by 0.7°C decade−1, while the average number of days with snow decreased by 5.0 d decade21, and the average snow depth on those days decreased by 1.7 cm decade−1. Breakup date and ice duration trends varied over the study area, with faster changes occurring in the southwest. Trends for each site were compared to static waterbody characteristics and meteorological variables and their trends. The trend toward later freeze date was stronger in large, low‐elevation waterbodies; however, freeze date trends had no geographic patterns or relationships to meteorological variables. Variability in the strength of trends toward earlier breakup was partially explained by spatial differences in the rate of change in the number of days with snow cover, mean snow depth, air temperature (warmer locations showed stronger trends), and rate of change in air temperature. Differences in ice duration trends were explained best by a combination of elevation and the local rate of change in either temperature or the number of days with snow cover.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".