Immersion during egg swelling results in rapid uptake of stable isotope markers in salmonid otoliths
Bibliographic record
Abstract
Determining the value of restocking wild fisheries with hatchery-reared fish requires the ability to identify and quantify the survival of hatchery fish after release. However, to obtain accurate estimates of survival rates, multiple fish identification techniques are often used, making the monitoring of restocking inefficient and costly. Here we test a new immersion marking method to determine its efficiency and cost effectiveness for marking millions of hatchery-reared Atlantic salmon (Salmo salar). Salmon eggs were marked during the egg swelling stage by immersing eggs in a solution containing seven enriched stable isotopes (134Ba, 135Ba, 136Ba, 137Ba, 86Sr, 87Sr, and 26Mg) for 2 h immediately after fertilisation. One hundred percent successful marks were detected in the otoliths of resulting larvae at a concentration of 1000 μg·L−1 for 136Ba and 100 μg·L−1 for 135Ba and 137Ba, with no detrimental effects on survival or health of egg and yolk sac larvae. We estimate that seven unique mark combinations can be made at a cost of $0.0001 to $0.0017 (US) per egg and conclude that marking via egg immersion is suitable for low cost, accurate marking of hatchery-reared salmonids destined for restocking purposes.
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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".