Evaluation of a Deepwater Release Method for Hatchery-Reared Lake Sturgeon
Bibliographic record
Abstract
Abstract Hatchery rearing and stocking of young Lake Sturgeon Acipenser fulvescens have become key management and species recovery tools in many jurisdictions, yet there has been relatively little investigation into the optimization of release methods. Based on the contemporary understanding of the species’ ecology in Boreal Shield rivers, it was hypothesized that poststocking survival and/or within-reach retention could be improved via release of hatchery-reared fish directly into the deepwater habitats that are preferred by juvenile and subadult life stages. Acoustic telemetry and mark–recapture methods were used to evaluate this hypothesis. In total, 390 PIT-tagged age-1 Lake Sturgeon (mean ± SD of length measured 1 week prior to stocking = 242 ± 29 mm FL) were released during spring 2015 from shore at the base of a set of falls on the Nelson River, Manitoba (mimicking the status quo stocking procedure for the reach), and 391 PIT-tagged individuals (240 ± 27 mm FL) were released approximately 4.2 river kilometers downstream of the shore release location into an approximately 750-m-long patch of deepwater habitat (maximum depth = 15 m) using the deepwater release method. Of the released fish, 30 (15 per stocking method) were implanted with acoustic transmitters. In total, 23% and 22% of the stocked fish were recaptured in gill-net surveys conducted during fall 2015 and 2016, respectively. Recapture proportions associated with the two release types were not significantly different. Acoustic telemetry revealed considerable variation among individuals and offered general insight into poststocking behavior but provided no direct support for the primary hypothesis. Despite the null results of the current study, optimization of release methods for Lake Sturgeon may warrant further investigation.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".