Interactive effects of substrate sand and silt contents, redd-scale hydraulic gradients, and interstitial velocities on egg-to-emergence survival of Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
We conducted laboratory incubation experiments with Atlantic salmon (Salmo salar) eggs to test a number of hypotheses concerning the relative sensitivity of the incubating eggs to variations in silt (diameter < 0.063 mm) in interaction with sand (0.63 mm < diameter < 2 mm) fractions in the incubating gravels, as well as to different strengths of local hydraulic gradients pushing flow across the egg pocket. Our results show that variations of only a few percent of silt content can strongly degrade survival to emergence. Higher silt loadings (>0.5%) are detrimental to survival for all substrate mixtures, except those that are very sparse in sands (<5%). For sand contents over 10%, an increment of 1% silt has over three times the effect on survival as a 1% increment in sand. Increasing hydraulic gradients had a positive effect on median survival, but the effect depended both on the details of the fines composition and on the gradient level. Our results suggest that silt loadings over 1.5% in redds cannot easily be mitigated by stronger gradients. Our data conclusively show that there is no single threshold interstitial flow velocity that insures survival to emergence. Even when maintaining a constant interstitial velocity, survival tended to reduce in higher fines-content substrate.
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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 | 0.001 | 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".