Corresponding author’s address:
Bibliographic record
Abstract
A weak-to-moderate El Niño event is developing over the equatorial Pacific Ocean. Meanwhile, the Pacific Decadal Oscillation (PDO) is flip-flopping between positive and negative phases in the last few months. This study focuses on using correlations of the antecedent El Niño/Southern Oscillation (ENSO) and PDO signals with the climatic variables of Vancouver in the following February and March, these being the time of the 2010 Vancouver Olympic and Paralymic Games respectively, to construct a predictive model with known skill. It is shown that significant early ENSO signals can indeed be detected in the Vancouver temperature records in February and March, with the maximum correlation coefficients occurring when the ENSO signals in June or July lead Vancouver temperatures in February for seven to eight months. These long-lead ENSO signals are modified by the PDO signals to some extent. Regression models based on the significant ENSO/PDO signals achieve meaningful scores for temperature predictions. Given the current El Niño and PDO conditions, the regression models suggest that the monthly mean temperature in Metro Vancouver will be about 0.6 to 1.1°C above normal in February 2010 and about to 0.6°C around normal in March 2010. In Metro Vancouver, the projecting precipitation amounts in February 2010 are in the range of 60–80 mm, with respect to the climatological mean and median of 113 mm and 107 mm, respectively. The projecting snowfall amounts in the same month are in the range of 0.0–1.3 cm, with respect to the climatological mean and median of 7.9 cm and 1.8 cm, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.878 | 0.798 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".