Using seasonal oscillations in fin ray microchemistry to chemically age Lake Sturgeon
Bibliographic record
Abstract
Abstract Objective Nonlethally sampled pectoral fin rays are commonly used as aging structures for Lake Sturgeon Acipenser fulvescens, but they tend to underestimate the true age in older, slow-growing individuals (age > 14 years). Current aging practices involve counting bands along the structure, which are construed as annuli. Oscillations of certain trace elements corresponding with annuli have been seen across various fish species, with patterns continuing to the marginal edge of hard structures. This study explored the aging of fin rays by using microchemistry patterns in Lake Sturgeon of known age (n = 94; ages 5–21) to determine the potential use of this method as an alternative or supplementary aging technique. Methods Elements were chosen for age determination analysis by examining the relationship between profile minima/maxima and visually interpreted annuli. Fish were assigned ages using three different methods: (1) traditional interpretation (counting annuli along the structure), (2) chemical interpretation (examination of seasonal variations in elemental profiles with visually identified annuli), and (3) a statistical model (multivariate multiple changepoint analysis with finite differencing using raw elemental profiles). Result Mean absolute differences between age estimates and known age were significantly higher for the traditional interpretation method than for the chemical interpretation method. The mean coefficient of variation in estimated age was 11.14% for the traditional interpretation method and 4.04% for the chemical interpretation method. The changepoint model was able to correctly classify age for 100% of the samples within ±1 year in one population (ages 5–8) but could not classify samples from the second population (ages 12–21). Conclusion Our results suggest that chemical aging techniques could provide more reliable age estimates for juvenile and subadult Lake Sturgeon when fin rays are the only aging option. Further work is required to determine the applicability of the model for assigning ages to older fish and for use with different populations and structures.
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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 | 0.001 |
| 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".