Otolith microchemistry to identify sources of larval yellow perch in a fluvial lake: an approach towards freshwater fish management
Bibliographic record
Abstract
The study aims at determining which spawning sites are contributing to yellow perch (Perca flavescens) juveniles’ recruitment in Lake Saint-Pierre (St. Lawrence River, Canada). We expect to highlight new management perspectives. Thus, we investigated both natal origin and connectivity processes for young of the year prior to their first winter. Otolith chemical composition was measured at larval and juvenile stages using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Five spawning sites were sampled and discriminated using a three-elemental model (manganese, barium, strontium). Results showed that (i) all within-lake sites contributed similarly to juvenile production and (ii) production results from both local recruitment and lake-wide connectivity processes. The study suggests fish management should include an overall evaluation of the lake-wide recruitment. Both local and widespread actions are required, depending on the level of connectivity in the lake, which plays a central role in shaping the spatial pattern of recruitment. Finally, otolith microchemistry proves to be an efficient tool for freshwater fish managers to evaluate both natal origin and connectivity in heterogeneous aquatic ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".