Comparison between two sampling methods to evaluate the structure of fish communities in the littoral zone of a Laurentian lake
Bibliographic record
Abstract
The results of beach seining were compared with visual surveys, in habitats showing a gradient of macrophyte densities in Lake Drouin, Québec, Canada. Six community descriptors (species density, total fish density, relative abundance per species, presence or absence of given species, size structure of the fish community and total biomass of the fish community) were used to compare the sampling methods. Most of the fish community descriptors obtained by visual surveys were estimated with an accuracy similar to that of beach seining. Both methods sampled the same number of species (eight out of nine). Visual surveys assessed the relative abundance of the yellow perch Perca flavescens and white sucker Catostomus commersoni with an higher accuracy than the beach seine. The greatest discrepancies between the two sampling methods were for total fish density and the total fish biomass. Because of the sampling strategy, both descriptors were underestimated by visual surveys, notably in the higher macrophyte density. In a broad community survey to determine the relative importance of species abundance, the visual survey was effective and could be used to develop a within‐lake regular and fine‐scale sampling design of the spatial arrangement of fish communities and their habitats.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".