Borrowing external information to estimate angler size selectivity: model development and application to Murray cod
Bibliographic record
Abstract
Estimating the size selectivity of fishery users and sampling methods can be difficult to achieve due to data limitations. However, these limitations can be moderated by borrowed information from other sources such as other systems, times, and species. Here we develop a model that integrates an externally sourced boat electrofishing length–vulnerability model with internally sourced boat electrofishing and angling catch data to estimate length-dependent vulnerability of fish to angling in a data-limited situation. We apply the model to Murray cod (Maccullochella peelii) as an example and show that angling for Murray cod selectively captures a narrow range of sizes that includes medium to large size fish. Although boat electrofishing also followed a similar pattern, the range of fish sizes vulnerable to capture was much broader, including a more uniform vulnerability of all size classes evaluated. Understanding the length selectivity to capture has key implications for effective determination of fisheries regulations, as well as interpreting monitoring data. Thus, we see this modelling approach as a good option when more informative data are not available to support the estimation process.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".