Comparison of fish size spectra obtained from hydroacoustics and gillnets across seven European natural lakes
Bibliographic record
Abstract
We conducted a systematic evaluation of the correspondence in fish length data obtained from vertical hydroacoustics and gillnetting across seven European natural lakes differing in abiotic and biotic characteristics. Length data were analyzed as continuous size spectra characterized by their maximum-likelihood estimated exponents b. First, we examined the relationship between size spectra obtained from the two sampling methods. We then examined whether size spectra from the two methods were correlated with lake descriptors separately or in combination. The modeled relationship between the exponents b from the two methods showed that the exponent b from the hydroacoustics was, on average, the same as that from the gillnet sampling in the seven lakes. The exponents b from the hydroacoustics and gillnets, when averaged, were significantly correlated with lake depth, while their differences were significantly correlated with mean air temperature. To conclude, the overall good correspondence between the continuous size distributions obtained by both methods supports the application of vertical hydroacoustics in acquiring size structure of fish communities in lakes, but not in fully replacing the invasive gillnetting. Yet, some specific methodological details require further research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".