Use of Underwater Visual Distance Sampling for Estimating Habitat-Specific Population Density
Bibliographic record
Abstract
Abstract We contrasted fish abundance estimates generated from mark–recapture and underwater visual distance sampling to determine whether the latter method is a potentially valuable fisheries assessment tool. We further examined whether altering the detection function or habitat stratification and including lake characteristics such as Secchi depth, temperature, and fish density affected distance sampling estimates. Distance sampling produced estimates that were comparable to those of mark–recapture techniques (r 2 = 0.60), and the relationship improved considerably when two species that were difficult to sample visually were removed from the analysis (r 2 = 0.88). The precision of mark–recapture estimates was significantly better than that of distance sampling. Distance sampling estimates were more similar to mark–recapture estimates when stratified by habitat than when pooled across habitats. The addition of Secchi depth, temperature, fish density, or a combination thereof to a regression equation that included mark–recapture and distance sampling estimates did not improve the strength of the relation. We were able to detect significant among-habitat differences using habitat-specific density estimates provided from distance sampling. Distance sampling is a less-intrusive means to determine fish abundance and microhabitat patterns and provides a way to determine age-0 fish abundance in addition to information (e.g., school size) that is not readily obtained by mark–recapture studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".