Acoustically derived fish size spectra within a lake and the statistical power to detect environmental change
Bibliographic record
Abstract
Fisheries acoustic surveys are increasingly being used to monitor the abundance of fish stocks, yet their adoption as a tool to monitor changes in community size spectra has not been well explored. In this study, we use a series of historical acoustic surveys of the pelagic zones of three arms of Lake Opeongo to determine if acoustically derived size-spectra indicators (slope and height) can be effectively measured and used as a monitoring tool. Acoustic size-spectra indicators were successfully measured for every survey and resembled the same indicators found in netting surveys. From 2005 to 2009, the slope of the size spectra became shallower, likely due to a decrease in abundance in schooling prey fish. Estimates of sources of survey variation, including fish size estimates and interbasin differences, were low (<10% coefficients of variation), suggesting that monitoring programs would detect annual changes in the size-spectra slopes ranging from 2% to 15% within 10 years. Size-spectra heights did not change very much over the time series of surveys. Using lake trout (Salvelinus namaycush) fishery data from Lake Opeongo, we estimate that sources of natural variation in size spectra at the population level could be much higher and potentially require longer monitoring periods. We noted from our study that standardized surveys and data analyses are critical if acoustically derived size spectra are to be adopted as a monitoring tool. We also suggest that size spectra be calculated through echo-integration methods rather than echo-counting methods because, in this study, echo counting led to lower estimates of abundance and size-spectra indicators, which are likely underestimates of the true fish community’s abundance.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".