Bibliographic record
Abstract
Abstract Large‐scale ice phenology studies have revealed overall patterns of later freeze, earlier breakup, and shorter duration of ice in the Northern Hemisphere. However, there have been few studies regarding the trends, including their spatial patterns, in ice phenology for individual waterbodies on a local or small regional scale, although the coherence of ice phenology has been shown to decline rapidly in the first few hundred kilometers. In this study, we extracted trends, analyzed affecting factors, and investigated relevant spatial patterns for ice breakup date time series at 10 locations with record length ≥90 years in south‐central Ontario, Canada. Wavelet methods, including the multiresolution analysis (MRA) method for nonlinear trend extraction and the wavelet coherence (WTC) method for identifying the teleconnections between large‐scale climate modes and ice breakup date, are proved to be effective in ice phenology analysis. Using MRA method, the overall trend of ice breakup date time series (1905–1991) varied from earlier ice breakup to later ice breakup, then to earlier breakup again from south to north in south‐central Ontario. Ice breakup date is closely correlated with air temperature during certain winter/spring months, as well as the last day with snow on the ground and number of snow‐on‐ground days. The influences of solar activity and Pacific North American on ice breakup were comparatively uniform across south‐central Ontario, while those of El Niño–Southern Oscillation, North Atlantic Oscillation, and Arctic Oscillation on ice phenology changed with distance of 50–100 km in the north‐south direction.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".