Seasonal Prediction of Killing-Frost Frequency in South-Central Canada during the Cool/Overwintering-Crop Growing Season
Bibliographic record
Abstract
Abstract Seasonal killing-frost frequency (KFF) during the cool/overwintering-crop growing season is important for the Canadian agricultural sector to prepare and respond to such extreme agrometeorological events. On the basis of observed daily surface air temperature across Canada for 1957–2007, this study found that more than 86% of the total killing-frost events occur in April–May and exhibit consistent variability over south-central Canada, the country’s major agricultural region. To quantify the KFF year-to-year variations, a simple index is defined as the mean KFF of the 187 temperature stations in south-central Canada. The KFF variability is basically dominated by two components: the decadal component with a peak periodicity around 11 yr and the interannual component of 2.5–3.8 yr. A statistical method called partial least squares (PLS) regression is utilized to uncover principal sea surface temperature (SST) modes in the winter preceding the KFF anomalies. It is found that most of the leading SST modes resemble patterns of El Niño–Southern Oscillation (ENSO) and/or the Pacific decadal oscillation (PDO). This indicates that ENSO and the PDO might be two dominant factors for the KFF variability. From a 41-yr training period (1957–97), a PLS seasonal prediction model is established, and 1-month-lead real-time forecasts are performed for the validation period of 1998–2007. A promising skill level is obtained. For the KFF variability, the prediction skill of the PLS model is comparable to or even better than the newly developed Canadian Seasonal to Interannual Prediction System (CanSIPS), which is a state-of-the-art global coupled dynamical system.
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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 | 0.000 |
| 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 | 0.000 |
| 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".