Pacific Storm Prediction Centre
Bibliographic record
Abstract
A weak-to-moderate El Niño event is developing over the equatorial Pacific Ocean, where monthly mean sea surface temperature (SST) anomalies were from +0.5°C to +1.5°C in July and August 2009. This study focuses on using correlations between antecedent El Niño/Southern Oscillation (ENSO) indices and the climatic variables in the following February and March, these being the time of the 2010 Vancouver Olympic and Paralymic Winter Games respectively, to construct a predictive model with known skill. In particular, the most significant cross correlations are between the temperatures of Vancouver in February and the NINO3 index in the preceding July. The temperatures in Vancouver in the spring season from March to May are also strongly correlated with various ENSO indices. Regression models based on these ENSO signals achieve meaningful scores for temperature predictions in February, March, and May. Predictions with the July 2009 El Niño condition suggests the monthly mean temperature of Metro Vancouver will be about 1°C above normal in February 2010 and near normal in March 2010. Less snowfall in Metro Vancouver is expected in February 2010 due to these warmer conditions. 1 1.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.190 | 0.100 |
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".