Characterizing north temperate lake littoral fish assemblages: a comparison between distance sampling and minnow traps
Bibliographic record
Abstract
To determine which sampling method may be best employed to monitor changes in fish species composition in littoral areas of small north temperate lakes (≤50 ha), data were collected using distance sampling, based on the line transect method, and a traditional fish sampling method of baited minnow traps. These methods were independently biased in terms of their estimates of species presence and abundance. However, analyses of fish community structure indicated that minnow trap data alone provided a better measure of community structure than did distance sampling data alone. Distance sampling, although a powerful tool in estimating population density, is hindered by a multitude of environmental variables that do not preclude the use of minnow traps. Lakes with clear water containing few species with abundant individuals are best suited for distance sampling. Minnow traps proved to be a fast, efficacious sampling method capable of fairly accurately defining the littoral small-fish community structure in most lakes sampled. These traps are a reliable method for monitoring presence or absence and relative abundance of small-bodied fish species in north temperate lakes. However, the combination of minnow traps and some form of visual sampling will help to ensure that all species are detected in small north temperate lakes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".