Effect of Different Levels of Fine-Sediment Loading on the Escapement Success of Rainbow Trout Fry from Artificial Redds
Bibliographic record
Abstract
Abstract Increased sedimentation from logging operations can affect water flow over salmonid redds, potentially impairing embryonic development in incubating eggs and fry. Effects may result from reduced oxygen delivery or waste removal or from physical entrapment of fry by sediment cap formation. A system of artificial redds was used to examine the effects of varying loads of mixed fine sediments on the escapement success of rainbow trout Oncorhynchus mykiss fry. Replicates of 80 eyed rainbow trout eggs were seeded in redds loaded with low (11.8%), medium (21.2%), high (28.6%), or no (0%) additional fine sediments. Interstitial oxygen saturation was determined in each chamber throughout incubation and emergence. Total percent emergence, residual yolk sac remaining, prevalence of deformities, and condition factor were determined in emergent fry. Fry began to emerge at 507.6 degree-days postfertilization, and overall emergence was more than 70% in all treatment levels. Emergence rate patterns approximated a normal distribution in all treatment groups except for the high-sediment-loaded redds. Emergence of fry from the high-sediment-load chambers was greater initially and exhibited a slower, more continuous pattern. The formation of a sediment cap in the redds with high-sediment loading appears to have significantly altered the emergence pattern of developing fry. Mean oxygen saturation was more than 79% in all groups and was not correlated to sediment load. Residual yolk was not different among fry from any of the groups throughout the emergence period. Fry condition factor decreased significantly over the sampling period, but there were no statistically significant differences among treatments.
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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".