Effects of flow fluctuations on habitat use and survival of age-0 rainbow trout (<i>Oncorhynchus mykiss</i>) in a large, regulated river<sup>1</sup>This article is a companion to Korman et al. 2011, published this issue.
Bibliographic record
Abstract
We evaluated effects of reduced hourly variation in flow from Glen Canyon Dam on survival of age-0 rainbow trout ( Oncorhynchus mykiss ) in the Colorado River, Arizona, USA, based on monthly abundance estimates. The proportion of the age-0 population in low-angle shorelines, which are potentially more sensitive to flow variability, declined from 70% in June to 20% in November as fish grew and made an ontogenetic habitat shift to deeper habitat. Average daily instantaneous mortality between August and September was 0.008 units lower in years when there was no change in the minimum flow compared with years when there was a sudden 50% reduction in the minimum flow. However, mortality was 0.006 units higher during the fall when there was no hourly variation in flow compared with years when flows fluctuated. As a result of these opposing patterns, 3-month age-0 survival across steady (0.31) and unsteady (0.28) flow regimes were very similar. While additional replication is required to strengthen inferences about effects of steadier flows, we demonstrate the utility of early life history monitoring for evaluating effects of flow management on fish populations in regulated rivers.
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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 | 0.000 |
| 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".