Evaluation of Calcein for Estimating Abundance of Lake Trout Alevins on a Spawning Reef
Bibliographic record
Abstract
Abstract Reproduction by stocked Lake Trout Salvelinus namaycush is generally estimated as the relative abundance of fry, that is, catch per unit effort in emergent fry traps and in beam trawls, but these estimates have high variance due to spatially heterogeneous distributions of fry. We used calcein, which produces a fluorescent mark in calcified structures, to batch-mark fry and generate a mark–recapture estimate of fry abundance on a small, shallow spawning reef. Eggs collected from feral Lake Trout in Lake Champlain, Vermont were reared at ambient lake temperatures, and fry were marked 7 d after hatching. Fry were immersed in a salt solution for osmotic induction and then placed for 4 min in a calcein solution. After marking, 18,000 fry were released on a spawning reef, and 2,000 fry were maintained in the hatchery. Wild-caught fry and hatchery fry were checked for marks every 2–9 d. Mark clarity was highest in the mandible and tail rays. Marks may have faded, but they did not disappear: marks were visible in the mandible in 100% of hatchery fry after 68 d. An average of 37% of wild-caught fry had marks, yielding a Chapman population estimate (±SD) of 47,486±2,301. The mark–recapture estimate was within the range of fry abundance estimated over 6 years based on egg density data and estimates of hatching success but was substantially higher than estimated for the same year-class. This work supports prior estimates of fry abundance and provides a potential method for assessing fry abundance on deep reefs and the success of fry stocking. Received June 4, 2013; accepted November 20, 2013
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".