Comparisons of Precision and Bias with Two Age Interpretation Techniques for Opercular Bones of Longnose Sucker, a Long-Lived Northern Fish
Bibliographic record
Abstract
Abstract Two preparations of opercular bones for estimating age of longnose suckers Catastomus catastomus revealed that annuli were obscured by dense bone and undetected when the whole operculum technique was used. By thin-sectioning the opercula, we removed the dense bone, revealing previously obscured annuli. Using opercula collected from older fish in Labrador, Canada, we found that the dense bone overgrowth led to an overall age bias of 1 year. When samples were broken into groups based on sectioned ages, however, there were minimal differences in age between the two techniques for fish age 6 and younger and a 2-year age difference for fish estimated to be age 10 and older. To compare precision in locating annuli between the two techniques, we calculated coefficient of variation values among independent determinations. Both techniques demonstrated low variance, however, age determinations had greater variation with thin-sectioning than with whole opercula interpretations. Therefore, we conclude that care must be taken when making annulus determinations from thin-sectioning. Due to the presence of dense bone overgrowth, associated with the whole operculum technique, we conclude a combination of both techniques would provide the most thorough procedure for interpreting opercular age for such long-lived fish (30–50 years). Received November 23, 2011; accepted April 5, 2012
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".