Comparison of Rainbow Smelt Age Estimates from Fin Rays and Otoliths
Bibliographic record
Abstract
Abstract Rainbow smelt Osmerus mordax, although nonnative, are an important component of the offshore food web in the Laurentian Great Lakes. In Lake Ontario, we estimate ages of rainbow smelt annually to study population dynamics such as year-class strength and age-specific growth and mortality. Since the early 1980s, we have used pectoral fin rays to estimate rainbow smelt ages, but the sectioning and mounting of fin rays are time and labor intensive. Our objective was to assess the feasibility of using otoliths rather than fin rays to estimate rainbow smelt ages. Three readers interpreted the ages of 172 rainbow smelt (60–198 mm total length) based on thin sections of pectoral fin rays, whole otoliths with no preparation, and whole otoliths that had been cleared for 1 month in a 70:30 ethanol : glycerin solution. Bias was lower and precision was greater for fin rays than for otoliths; these results were consistent for comparisons within readers (first and second readings by one individual; three readers were used) and between readers (one reading for each reader within a pair). Both otolith methods appeared to misclassify age-1 rainbow smelt. Fin ray ages had the highest precision and provided the best approximation of age estimates inferred from the Lake Ontario population's length frequency distribution and from our understanding of this population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".