Utilizing random forest analysis with otolith mass and total fish length to obtain rapid and objective estimates of fish age
Bibliographic record
Abstract
Age estimates from otolith morphometrics represent a rapid and objective alternative to traditional ageing techniques though use has been limited to marine and short-lived freshwater species. We utilized random forest analysis with otolith mass, total length, and several temporal and spatial predictor variables to assess variable importance and accuracy of age estimates for age-0 through age-11 yellow perch (Perca flavescens) in southwestern Lake Michigan. Accuracy of age predictions decreased with increasing age as 95% of juvenile (age-0 through age-2) ages were predicted correctly compared with 55% for adults (age-3 through age-11). Precision of age estimates within 1 year of reader-assigned age were high for both juvenile and adult yellow perch at 100% and 86%, respectively. Otolith mass was the most important predictor variable; however, substantial overlap existed among adult ages. Random forest analysis utilizing otolith mass, total length, and other pertinent predictor variables represents an applicable tool to reduce subjectivity and resource expenditure while providing accurate age estimates for juvenile and short-lived fishes.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".