Statolith microchemistry as a technique for discriminating among Great Lakes sea lamprey (Petromyzon marinus) spawning tributaries
Bibliographic record
Abstract
Laurentian Great Lakes fishery management agencies are seeking ways to identify natal origins of parasitic- and spawning-phase sea lamprey ( Petromyzon marinus ) so that efforts to control this invasive species can be prioritized. We developed laser-ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) as a technique to quantify elemental concentrations in larval sea lamprey statoliths and explored the use of statolith microchemistry as a tool to discriminate among larval sea lamprey production streams. Our analyses demonstrate that (i) traversing across the statolith with the laser is preferable to drilling down through its apex, (ii) preserving specimens in 95% ethanol versus freezing them has minimal effects on elemental concentrations, (iii) a minimum of 15 individuals per stream should accurately depict stream-specific statolith elemental signatures, and (iv) LA-ICP-MS is preferable to particle-induced X-ray emission (PIXE) for statolith analysis, based on higher precision, lower cost, reduced sampling-time requirements, and wider availability. Using LA-ICP-MS, we could discriminate among larvae from 13 streams located in Lakes Michigan, Huron, and Superior with 82% classification accuracy, indicating that this tool holds promise for determining natal origins of sea lamprey in the Great Lakes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".