Forecasting Fraser River flows and temperatures during upstream salmon migration
Bibliographic record
Abstract
Mature salmon returning to spawn in their natal streams are sensitive to both river flows and temperatures. Enhancements to existing forecast models result in significant reductions in the root mean square (RMS) forecast errors for both flow and temperature in the semi-weekly 10-day forecasts made during the salmon migration season. The Fraser watershed model is replaced by a statistical model that projects future flows using the latest observation as an initial condition and a slope consistent with the historic rate of change. This new method reduced RMS errors by as much as 38%. When the model flows were adjusted iteratively by feeding the flow error back into the system, the average RMS forecast flow error was reduced from 18.7% to 6.4% in 2000 and from 16.8% to 7.4% in 2002. The coupled temperature model combines atmospheric forcing with transport and velocities from the flow models. When the temperature model was run using the iterative feedback scheme, the RMS forecast error was reduced from 0.85 °C to 0.59 °C in 2000 and from 1.18 °C to 0.94 °C in 2002. Key words: Fraser River, temperature model, flow model, data assimilation, salmon.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".