Transactions of the American Fisheries Society 125:889-898. 1996 €> Copyright by the American Fisheries Society 1996 Back-Calculation of Fish Length from Scales: Empirical Comparison of Proportional Methods
Bibliographic record
Abstract
Abstract.—We compared three proportional back-calculation methods for scales using data sets for pumpkinseeds Lepomis gibbosus and golden shiners Notemigonus crysoleucas from 10 southern Quebec lakes, and we validated back-calculations by comparing them with observed lengths at lime of annulus formation. Ordinary least-squares regression (OR) was compared with geometric mean regression (GMR) for describing body-scale relationships. Although minor differences were detected in body-scale regressions among lakes, pooling data across lakes yielded linear body-scale relationships with very high r2. Differences between OR and GMR body-scale relationships were negligible in both species. Likewise, all back-calculation methods produced equivalent results. Back-calculated lengths generally corresponded well with observed lengths in all pumpkinseeds age-classes and in golden shiners older than 1 year. Observed lengths were often greater than back-calculated lengths for age-1 golden shiners. Our results, indicating little or no difference among methods, contradict recent reviews claiming substantial disagreement among methods. Tighter body-scale relationships in our data sets than in previous studies appear to explain this contradiction. We suggest that light body-scale relationships are attainable for many species, obviating concern over which proportional back-calculation method is chosen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.284 | 0.147 |
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".