Forecasting Spring Reservoir Inflows in Churchill Falls Basin in Québec, Canada
Bibliographic record
Abstract
The performance of different models and procedures for forecasting aggregated May–July streamflow for the Churchill Falls basin on the Québec-Labrador peninsula is compared. The models compared have different lead times and include an autoregressive model using only past streamflow data, an autoregressive with exogenous input model utilizing both past streamflow and precipitation, and a linear regression model using the principal components of exogenous measures of atmospheric circulation inferred from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project. The forecast skills of the different approaches are compared using a variety of measures of performance. The results indicate that relatively accurate forecasts using only measures of atmospheric circulation can be issued as early as in December of the prior year. A multimodel combination approach is found to be more effective than the use of a single forecast model. In addition, it is concluded that forecasting models utilizing atmospheric circulation data are useful, especially for basins where hydroclimatic observations are scarce and for basins where flows and other hydroclimatic variables are not strongly autocorrelated (do not depend on their past).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".