Using tree-ring crossdating techniques to validate annual growth increments in long-lived fishes
Bibliographic record
Abstract
We applied crossdating, a dendrochronology (tree-ring analysis) age validation technique, to growth increment widths of 50 Sebastes diploproa otoliths ranging from 30 to 84 years in age. Synchronous growth patterns were matched by the following: (i) checking the dates of conspicuously narrow growth increments for agreement among samples and (ii) statistically verifying that growth patterns correlated among samples. To statistically verify pattern matching, we fit each time series of otolith measurements with a spline, and all measurements were divided by the values predicted by the curve. This standardized each time series to a mean of 1, removing the effects of age on growth and homogenizing variance. Each time series was then correlated with the average growth patterns of all other series, yielding an average correlation coefficient (r) of 0.53. Average growth of all 50 samples was significantly correlated with an upwelling index (r = 0.40, p = 0.002), the Pacific Decadal Oscillation (r = 0.29, p = 0.007), and the Northern Oscillation Index (r = 0.51, p = 0.0001), corroborating accuracy. We believe this approach to age validation will be applicable to a wide range of long-lived marine and freshwater species.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".