Identification of Summer-Run Chinook Salmon Spawning Areas in the Main-Stem Columbia River Upstream of Wells Dam, Washington
Bibliographic record
Abstract
Abstract Summer-run Chinook Salmon Oncorhynchus tshawytscha historically spawned in the Columbia River as far upstream as British Columbia, Canada, but the upstream extent of main-stem spawning in the contemporary Columbia River has been documented in the tailrace of Wells Dam, Washington. We utilized radiotelemetry and video monitoring equipment between 2011 and 2013 to track wild summer-run Chinook Salmon upstream of Wells Dam and document any main-stem spawning sites. Two small spawning areas totaling 3,389 m2 were identified within 1.76 km downstream of Chief Joseph Dam and were used by spawning salmon in each year of the study. Redd deposition totaled 70, 59, and 134 redds in 2011, 2012, and 2013, respectively. When considered with tributary redd counts upstream of Wells Dam from the Methow and Okanogan rivers, main-stem spawning represented between 1.6% and 2.9% of the annual redd deposition upstream of Wells Dam within each spawning year. Redds were located in depths between 3.7 and 7 m, with spawning occurring between late October and mid-November in each year. These novel results are the first to detail main-stem spawning locations of summer-run Chinook Salmon upstream of Wells Dam and should inform population monitoring metrics, such as annual escapement and prespawn mortality estimates.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".