The relation between age-0 rainbow trout (<i>Oncorhynchus mykiss</i>) abundance and winter discharge in a regulated river
Bibliographic record
Abstract
We identified and experimentally tested a dischargeabundance relation that predicted, based on the mean river discharge in the second half of winter (15 January 31 March), the spring abundance of age-0 rainbow trout (Oncorhynchus mykiss) in a section of the Henrys Fork of the Snake River, Idaho, with complex bank habitat. We also considered a competing hypothesis in which autumn abundance determined spring abundance. We established that large abundances of age-0 trout were present in autumn (34 000 81 000) and lower abundances remained in spring (8000 15 000). Winter loss of age-0 trout was initiated in January. Spring abundance in 19961998 was related to autumn abundance (r2 > 0.99) and mean discharge in the second half of winter (17.122.8 m3·s1; r2 > 0.99) but not mean discharge in the first half of winter (15.121.1 m3·s1; r2 = 0.11). We experimentally maintained a high discharge (2021 m3·s1) in the second half of winter in 1999 to test model predictions. Autumn abundance failed to predict spring abundance (observed = 11 109; predicted = 6822; 95% prediction interval = 46698975). However, the dischargeabundance model accurately predicted spring abundance (predicted = 11 980; 95% prediction interval = 10 728 13 231). Higher discharge in the second half of winter may have provided more bank habitat at a critical time for survival.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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 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".