Fish length back-calculation from scales: advancing methodology and correction of bias
Bibliographic record
Abstract
Fish length back-calculation from hard structures (e.g., scales, otoliths, spines, etc.) is a commonly used method to reconstruct individual growth rates and sizes at age/stage in the absence of an intensive sampling–resampling timeseries. However, reliable estimates via these methods require the empirical validation of multiple assumptions about the growth of these hard structures. Here, we focus on reducing bias in scale-based back-calculations and validating proposed improvements using archived scales from a wild population of anadromous steelhead trout ( Oncorhynchus mykiss). We first describe a series of back-calculation problems and propose solutions that can be integrated into common back-calculation methods. We then compare back-calculation accuracy, precision, and bias between proposed solutions and traditional forms of two back-calculation methods: Fraser–Lee and Body-Proportional. We discovered that the assumption that rapid growth begins immediately after juvenile steelhead pass downstream of the fish fence (300 m upstream of the ocean) was invalid and required a correction factor to prevent overestimating fish length by an average of 14%. The proposed modified methods we described performed better than traditional back-calculation methods. Overall, these findings can improve estimates of fish length from scale-based back-calculations and illustrate the importance of validating key assumptions.
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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 | 0.036 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".