Seasonal Prediction with Error Estimation of Columbia River Streamflow in British Columbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large-scale climatological states [tropical Pacific sea surface temperatures (SST), Pacific-North American (PNA) atmospheric teleconnection and Pacific Decadal Oscillation (PDO)] and local precipitation data are used to predict the April–August Columbia River streamflow at Donald, British Columbia, Canada. Using predictors up to the end of November in the preceding year, forecasts of the April–August streamflow were made by multiple linear regression (MLR) under a jackknife scheme. A correlation skill of 0.52 is attained using PDO, PNA and SST as predictors, with PDO being the strongest and SST the weakest. When local precipitation is added among the predictors, PDO becomes redundant, and MLR with precipitation, PNA and SST as predictors attained a correlation skill of 0.70. Feedforward neural-network models were used for nonlinear regression, but the results were essentially identical to the MLR predictions, implying that the detectable relationships in the short, 49-sample record are linear. A bootstrap process estimates the relative errors of the MLR predictions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it