Matching the elemental fingerprints of fish otoliths with water masses of the St. Lawrence River and its tributaries, Canada
Bibliographic record
Abstract
The physicochemical heterogeneity of large river ecosystems generates a mosaic of habitats that support diverse fish communities. Understanding the connectivity between habitats and exploited fish populations is necessary for the sustainable management of fisheries. This study evaluates the spatial concordance between elemental fingerprints of fish otoliths and surface waters in the St. Lawrence River (Canada) and its tributaries. We sampled 16 tributaries and various freshwater habitats of the St. Lawrence River and collected 136 water samples and 930 fish, representing 21 species. We observed a high spatial variability of trace element composition in the water and fish otolith samples. Reclassification using Sr and Ba concentrations of the collected water and otoliths was sufficiently accurate to correctly assign most fish to their capture region. We also observed significant variation in the elemental deposition relationships for Sr and Ba among fish families. The development of this elemental fingerprint reference database is fundamental for understanding the structure of exploited fish populations, validating the connectivity among habitats, and reconstructing habitat use by invasive fish species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".