Validation and Verification Field Trials of Air Diffuser Systems for Upwelling Applications
Bibliographic record
Abstract
Abstract As a consequence of climate change, the ocean surface temperature is rising. Warmer water has low dissolved oxygen, which can adversely affect the health of fish and their survival rates in aquaculture operations. Upwelling is a process in which deeper, colder water is lifted to the surface providing thermal mixing and circulation of oxygen-rich water. This paper discusses the design and execution of field trials to validate the performance of CanadianPond air diffuser upwelling systems for use in the aquaculture industry. Field trials were carried out at The Launch, an ocean innovation hub owned by the Marine Institute, in Holyrood Bay, NL, Canada. The upwelling systems were deployed at 20 m water depth and tested with 10 to 100 Standard Cubic Feet per Minute (SCFM) air flow rates. An electromagnetic current meter was used to measure the vertical and horizontal velocities generated by the upwelling systems at various locations underwater. Water depth-temperature profiles and dissolved oxygen levels were also measured prior to and during the trials. A Computational Fluid Dynamics (CFD) model was developed in parallel to assess the upwelling performance in different conditions and scales. The study concluded that localized upwelling was possible using the air diffusers tested, with effectiveness varying based on the applied air flow rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".