Relating fish distributions to physical characteristics of a tidal energy candidate site in the Banks Strait, Australia
Bibliographic record
Abstract
With the tidal energy industry moving towards commercial-scale developments, it is important to consider potential interactions between tidal energy converters (TECs) and the marine environment prior to the instalment of large-scale TEC arrays. The Banks Strait, a tidal channel located in the northeast of Tasmania, Australia, was identified as a promising candidate site for tidal energy by the Australian Tidal Energy (AUSTEn) project. To gain an understanding about potential overlap between TEC arrays and fish usage of the Banks Strait tidal channel, fish density distributions were estimated from hydroacoustic surveys during the tidal resource characterization campaign. Differences in fish density were examined according to bottom–depth, bottom – type, current speed, temperature and vertical distribution. Fish densities were significantly higher at night and displayed preferences for depths between 20 – 40 m and current speeds between 1.75 – 2 m/s. Fish density was generally highest in the bottom 10 m from the sea floor at all depths sampled. Variation by temperature and bottom–type sampled was not significant. Future studies involving long-term, stationary surveys of fish densities along with repeated surveys across different seasons would provide a more wholistic picture of fish distributions in the Banks Strait to inform developers about potential device encounter probabilities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".