A Comparison of Methods for Sampling round Goby in Rocky Littoral Areas
Bibliographic record
Abstract
Abstract Invasion of the round goby Neogobius melanostomus in the Great Lakes has prompted investigation into qualitative and quantitative sampling strategies. Design of an optimal sampling strategy to monitor expanding round goby populations should consider the accuracy, precision, and associated costs of gears and their deployment strategies. The goal of this study was to compare three common, low-cost, readily available gear types used to sample round goby (gill nets, minnow traps, and trotlines) in terms of catch rates, size selectivity, and bycatch. During fall assessments, baited minnow traps were the most effective at collecting round goby, followed by trotlines and monofilament gill nets. Minnow traps were more selective of small round goby, while trotlines and gill nets were more selective of larger individuals. Bycatch associated with minnow traps and trotlines was lower than that associated with gill nets. Seasonal variation in gear effectiveness was apparent: during spring, gill nets caught 216 round goby and minnow traps caught none, while during the fall only 11 round goby were captured in gill nets and 868 were captured in minnow traps. During summer assessments, diel movements were evident, as gill nets set during periods of low or no light captured more round goby than those set during the day. The cost-benefit analysis indicated that passive gears had lower scores (i.e., were more cost-effective) than active gears. The financial cost of the three gears indicated that the cost per round goby varied among gears and seasons. Our results demonstrated that baited minnow traps set overnight were the most efficient, easily deployed, cost-effective gear and exhibited less variable catch rates during late summer and fall than either gill nets or trotlines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".