Jellyfish Deflection From Marine Fish Pens Using Bubbler Technology
Bibliographic record
Abstract
Abstract Increased jellyfish blooms have been identified as a major cause of caged fish damage in marine aquaculture facilities as well as clogging of seawater intake systems of nuclear power plant cooling stations and desalination plants. Bubbler technology has shown great promise for protecting marine facilities from jellyfish impacts. This paper focuses on the design of apparatus, planning and execution of large scale tests using simulated silicone jellyfish in a controlled environment. The following parameters were varied during testing: water current (0.1–0.6 m/s), bubble curtain angle (5°, 30° and 45°), air flow rate (0.1, 0.3 and 0.6 cfm/ft), number of curtains (1, 2 and 3), jellyfish size (5 and 18 cm diameter), curtain spacing (15, 50, 100 and 400 cm) and jellyfish release methods (surface and subsurface). From the test results, it was concluded that effective deflection of jellyfish in marine environment with currents upto 0.6 m/s was achievable. Also, the curtain angle, number of curtains, larger spacing between curtains and air flow rate through bubbler were observed to have a positive impact on the deflection efficiency for high current speeds. A CFD model developed was used to estimate the deflection efficiency, which provided a good agreement with test results.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".