Otoliths, increments, and elements: keys to a comprehensive understanding of fish populations?
Bibliographic record
Abstract
The chronological properties of otoliths are unparalleled in the animal world, allowing accurate estimates of age and growth at both the daily and the yearly scale. Based on the successes of calcified structures as environmental proxies in other taxa, it was logical that researchers should attempt to link otolith biochronologies with otolith chemistry. With the benefit of hindsight, this anticipation may have been naive. For instance, the concentrations of many elements are lower in the otolith than in corals, bivalves, seal teeth, or the other bony structures of fish, making them less than ideal for elemental analyses. Nevertheless, there is growing interest in the use of otolith chemistry as a natural tag of fish stocks. Such applications are directed at questions concerning fish populations rather than using the fish as a passive recorder of the ambient environment and do not rely upon any explicit relationship between environmental variables and otolith chemistry. The questions that can be addressed with otolith chemistry are not necessarily answerable with genetic studies, suggesting that genetic and otolith studies complement rather than compete with each other. Thus, we believe that otolith applications have the potential to revolutionize our understanding of the integrity of fish populations and the management of fish stocks.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".