Variation between Electrofishing and Otter Trawling for Sampling Black Crappies in Two Florida Lakes
Bibliographic record
Abstract
Abstract We compared trawling with electrofishing for assessing populations of black crappie Pomoxis nigromaculatus in two shallow central Florida lakes. Lakes Jackson and Weohyakapka were sampled with both gears from November 2005 through March 2006. Size-selective biases were apparent, the trawl nets consistently collecting small individuals less than 100 mm in total length (TL), whereas electrofishing collected significantly larger, more adult fish exceeding 200 mm TL. The Kolmogorov–Smirnov two-sample test indicated that the size structure of the fish captured in the two types of gear differed significantly. The total annual mortality estimates were 28% and 72% for electrofishing on both lakes, 55% for otter trawls on Lake Jackson. The age ranges for catch curves derived from the two gears also displayed large differences in mortality estimates or gave no mortality estimate at all. Trawls tended to collect younger age-classes, whereas electrofishing tended to collect larger age-classes; thus, data collected with the two gears may represent different aspects of mortality in the population. When aquatic macrophyte cover is low so that both gears can be used, we recommend that otter trawl sampling be used to assess the recruitment of juvenile crappies and that simultaneous electrofishing be conducted to collect larger fish for the determination of age and growth. Fisheries biologists should be careful when using just one gear to evaluate crappie populations given that the size-selective biases associated with both trawling and electrofishing can influence basic mortality and length-frequency assessments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".