Acoustic telemetry system as a novel approach for evaluating the effective attraction of fish to artificial reefs
Bibliographic record
Abstract
Due to there being a lack of suitable approaches for evaluating the effectiveness of artificial reefs, two experiments were designed to examine the feasibility of acoustic telemetry, a rapidly developing method for tracking aquatic animals. The first experiment was conducted to understand the deployment procedures of an acoustic telemetry system and determine the appropriate deployment of receivers’ spacing, while the second experiment was conducted to quantify the site fidelity and habitat use of 11 reef fish in the Fangchenggang artificial reef area in the northern South China Sea, China. The results indicated that the logistic regression model was an effective way to balance the detection probability at different distances between the range test transmitter and receiver, with above 50% detection probability within 240 m and 80% detection probability within 110 m. The residency index, as a quantification of site fidelity, was 0.85 ± 0.24. The 100% minimum convex polygon, 95% kernel utilization distribution, and 50% kernel utilization distribution, which are the indicators of habitat use, were 34,522.94 ± 35,548.95, 1,467.52 ± 1,619.05, and 236.01 ± 294.59 m2, respectively. High site fidelity and the small spatial scale of habitat use for reef fish demonstrated that artificial reefs were an effective man-made structure to attract fish. Overall, this study supports the feasibility of the acoustic telemetry system, indicating that it provides a good approach for quantifying the associations between artificial reefs and fish.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".