Validation of three back-calculation models by using multiple oxytetracycline marks formed in the otoliths and scales of bluegill × green sunfish hybrids
Bibliographic record
Abstract
We assessed the accuracy of the FraserLee, biological-intercept, and Weisberg back-calculation models to estimate growth from otoliths and scales of laboratory-reared juvenile bluegill × green sunfish hybrids (Lepomis macrochirus × Lepomis cyanellus). Hybrid sunfish were injected three times with oxytetracycline hydrochloride at 90-day intervals to mark bony structures, creating simulated annuli for model validation. Back-calculated lengths (BCLs) with otoliths were generally less accurate than scales for all three models. Errors ranged from 8.2 to 7.8% for the FraserLee model, from 8.0 to 8.3% for the biological-intercept model, and from 6.5 to 14.3% for the Weisberg model. For all three models, there was no significant difference in BCLs using left or right otoliths, and BCLs using the FraserLee and biological-intercept models were not significantly different from each other. In contrast with otoliths, all three models produced accurate BCLs from scales; errors ranged from 4.3 to 0.1%. For juvenile hybrid sunfish, we recommend using scales for back-calculation of growth. The FraserLee (with zero intercept) and biological-intercept models produced the most accurate BCLs from otoliths. However, due to potential decoupling of otolith and body growth, caution should be exercised when estimating juvenile hybrid sunfish growth from otoliths.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".