Validation of annual growth rings in freshwater mussel shells using cross dating
Bibliographic record
Abstract
We examined the usefulness of dendrochronological cross-dating methods for studying long-term, interannual growth patterns in freshwater mussels, including validation of annual shell ring formation. Using 13 species from three rivers, we measured increment widths between putative annual rings on shell thin sections and then removed age-related variation by standardizing measurement time series using cubic splines. Initially, cross dating was a valuable quality control technique allowing us to correct interpretive and measurement errors in 16% of specimens. For all species, growth varied among years but was highly synchronous among individuals. Standardized measurement time series of 94% of individuals were significantly correlated with species master chronologies, and mean interseries correlations ranged from 0.37 to 0.96. Growth was also synchronous among species, even from different rivers, and growth was negatively correlated with mean annual streamflow for most species except Quadrula pustulosa from a regulated dam tailrace. Highly synchronous growth and the strong relationship to streamflow showed that large-scale environmental signals generated non-age-related variation in mussel growth giving strong support for annual formation of the growth increments we measured. Cross dating can be a valuable technique for studying freshwater mussel growth providing quality control, validation of annual rings, and reconstruction of long-term growth histories.
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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 | 0.009 |
| 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 | 0.000 |
| 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".