Gaat (sockeye salmon, Oncorhynchus nerka) migration up the Gaat Héeni (Silver Salmon River): Influence of atmospheric rivers on hydrologic variability
Bibliographic record
Abstract
,Atmospheric rivers (ARs) drive hydrometeorological variability, influencing precipitation, river discharge and water temperature. This study quantifies how ARs contribute to precipitation and hydrology in the Gaat Héeni (Silver Salmon River) Watershed, a key migration corridor for Sockeye Salmon. I integrate ERA5-Land reanalysis and the SIO-R1 AR catalog with in-situ hydrometric measurements and biological monitoring data, including video observations at a waterfall barrier and escapement counts from a weir. This approach enables me to evaluate how AR-driven changes in hydrologic conditions subsequently influence Sockeye Salmon migration success. Sockeye Salmon jump success, modelled using logistic regression, was primarily influenced by river discharge, with peak success occurring at 10–12 m³ s⁻¹ discharge levels. Jump success at SR3-3 declined at discharge levels above 16 m³ s⁻¹ or below 9 m³ s⁻¹. Although water temperatures of 13–15 °C coincided with optimal jump success, water temperature was not a statistically significant predictor, reducing confidence in its influence relative to discharge. Results show that AR events contributed between 15.9% and 39.1% of seasonal precipitation from 1991 to 2023, with the highest contributions in fall (37.9%) and winter (24.1%). During the 2024 monitoring season, an AR event on 22–24 July triggered a discharge surge from 8.7 to 30.0 m³ s⁻¹ within two days, reflecting the watershed’s rapid hydrologic response. Centroid lag analysis revealed a median discharge lag of 1.6 days following AR-driven precipitation, underscoring the sensitivity of discharge timing to ARs.
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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 | 0.000 |
| 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) | 0.001 | 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".