Fish community size spectra and the role of vessel avoidance in hydroacoustic surveys of boreal lakes and reservoirs
Bibliographic record
Abstract
Hydroacoustic data were used to quantify vessel avoidance by fishes, and derive fish community size spectra in two shallow boreal systems in eastern Manitoba, Canada. Lac du Bonnet reservoir and adjoining lakes at Nopiming Provincial Park were studied during summer 2011 and 2012. The magnitude of boat avoidance varied between these relatively similar water bodies (p = 0.04), but was not significantly influenced by fish depth or survey speed. Length-frequency spectra were determined from acoustic surveys at Lac du Bonnet, and acoustic data were used to map bathymetry of the reservoir. Community abundance (spectra height) was greater in 2011 then 2012 (p < 0.05), and decreased through the summer. Spatial variation in spectra parameters appear to be related to physical habitat characteristics. I conclude that vessel avoidance should be quantified in situ, and that acoustic size spectra may be used to monitor differences in fish communities over time and among 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".