Modeling Effects of Length Limit Regulations on Riverine Populations of Channel Catfish
Bibliographic record
Abstract
Abstract Channel Catfish Ictalurus punctatus is an important species among anglers in North America. Channel Catfish management strategies vary greatly in the United States and Canada, and there are few examples of regulations aimed at improving size structure in the literature. The goal of this study was to model the response of Channel Catfish to changes in regulations that meet specific management objectives. We modeled a suite of regulations to determine if minimum length limits or a protected slot limit could be used to improve size structure or avoid growth overfishing of Channel Catfish in a large river. We modeled three minimum length limits (304, 356, and 406 mm) and a 356–456-mm protected slot limit to assess the effectiveness of each at delaying or eliminating growth overfishing and increasing the number of quality-sized fish in the Missouri River, Nebraska. Our models indicated a 406-mm minimum length limit could be used to avoid growth overfishing until exploitation rates were greater than 45% and that both minimum length limits and slot limits could be used to increase the number of quality-sized fish by up to 4 times the number under current regulations. Received April 6, 2015; accepted October 27, 2015
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".