Otolith microchemistry as a stock identification tool for freshwater fishes: testing its limits in Lake Erie
Bibliographic record
Abstract
We evaluated otolith chemistry as a tool for identifying natal origins of potamodromous fishes using historical Lake Erie water chemistry (1983–2001) and yellow perch ( Perca flavescens ) otolith elemental composition (1994–1996) data. Lake Erie’s tributaries had stream-specific chemical signatures that were temporally stable. Correspondingly, the otolith microelemental composition of larvae collected from tributary embayments (Sandusky and Maumee bays) was shown to be geographically distinct and the use of known-origin juveniles showed that larval otolith microelemental signatures could be used to accurately identify natal origins and indicate fish movement. Discrimination between offshore spawning locations was relatively difficult, however, indicating limitations to working in systems that are dominated by flow from a single large river (i.e., Detroit River). Interannual variability in otolith microelemental signatures was high such that larvae from one year could not reliably classify natal location of larvae in another year. Development of an annual library of site-specific signatures and exploration of complementary ways to discriminate natal origins would improve the use of otolith microchemistry as a fishery management tool in freshwater systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".