The effect of suspended sediment on fertilization success in sockeye (<i>Oncorhynchus nerka</i>) and coho (<i>Oncorhynchus kisutch</i>) salmon
Bibliographic record
Abstract
Suspended sediment is a naturally occurring part of aquatic ecosystems, and unnatural elevation of suspended sediment in streams above background concentrations is known to adversely affect Pacific salmon (Oncorhynchus spp.) at different life stages. Conspicuously lacking in the literature is knowledge of the effect of suspended sediment on egg fertilization success during spawning. Sockeye (Oncorhynchus nerka) and coho (Oncorhynchus kisutch) salmon gametes were used to investigate the impact of different concentrations of suspended sediment particles on the ability of sperm to successfully fertilize eggs. The fertilization process was simulated using a controlled-flow water flume with concentrations of suspended particles ranging from 800 to 47 000 mg·L–1. Egg fertilization success was assessed at eyed stage after incubating eggs in the streambed in the field or in artificial laboratory egg incubation (Heath) trays. Regression model analysis showed that suspended sediment concentrations reduced the percent of fertilized eggs and that reduction in successful fertilization below 80% could occur when suspended sediment levels are in excess of 9000 mg·L–1. The relationship derived herein serves as a preliminary guideline for determining the effect size of suspended sediment concentration on egg fertilization success during spawning.
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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) | 0.001 | 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".