Optimal sampling effort required to characterize wetland fish communities
Bibliographic record
Abstract
Wetlands are increasingly in peril as a result of human activities. In the Laurentian Great Lakes, coastal wetlands provide essential habitats for many fishes. Consequently, efficient sampling approaches for wetland fishes are needed for effective management. We employed a repeat-sampling strategy using a seine to collect fishes from seven wetlands. The data set was used to develop guidance for optimizing wetland fish sampling. To meet richness targets, the required number of sampling sites decreases as sampling intensity increases. Half the number of sites was required when three seine hauls per site were done compared with one haul. On average, 97 one-haul sites were required to detect 90% of species, whereas only 47 three-haul sites were required. Sampling effort is predicted to be greater in areas with more species and larger wetlands. The number of individuals and sites needed to detect 90% of species increased exponentially as species richness increased, and the number of individuals needed was positively related to wetland area. The use of block nets did not improve species detection or affect the composition.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".