Development of a Riverine Index Netting Protocol: Comparisons of Net Orientation, Height, Panel Order, and Line Diameter
Bibliographic record
Abstract
Abstract We developed a gillnetting protocol by sampling 17 nonwadeable rivers across northern Ontario over the course of four summers from 2005 to 2008. The rivers represented a range of habitats; however, all had a coolwater to warmwater fish community characterized by walleyes Sander vitreus, northern pike Esox lucius, white suckers Catostomus commersonii, yellow perch Perca flavescens, and smallmouth bass Micropterus dolomieu. During the study, three net designs were used. Each net had one to four configurations that varied in height, length, monofilament diameter, and sequence of the mesh sizes. We found that most fish (87%) were captured in the lower half of 1.8-m-high gill nets and that there was no difference in catch between gill nets with serial panels (i.e., sequentially increasing mesh sizes) and those with randomly organized panels. Net that were set perpendicular to shore or angled to shore caught approximately twice as many fish as nets that were set parallel to shore. Contrary to expectations, there were no differences in net durability and catch between thick- and thin-diameter monofilament nets. Based on the results of these experiments, we propose an improved net design that we call the large-mesh riverine index net for use in rivers; it is more versatile, reduces net drag, and fishes more effectively than the traditional gill nets that are used in lakes. The large-mesh riverine index net has eight panels (3.1 m long × 0.9 m high) and totals 24.8 m in length. Panel stretch measures (and the random order used) for this design are 127, 76, 178, 25, 102, 203, 51, and 152 mm. The nets are set perpendicular to shore in rivers from July 1 to October 1 and should be fished in areas where water velocity is less than 0.1 m/s. Received January 12, 2010; accepted November 2, 2010
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".