Effect of Shading Ratio on Japanese Sea Bass (Lateolabrax japonicus) and Asian Sea Bass (Lates calcarifer) Aquaculture
Bibliographic record
Abstract
Floating photovoltaic arrays on ponds may alter thermal and optical conditions that are relevant to aquaculture performance. This study compared 0% and 40% surface shading in two outdoor earthen-pond trials, one with Asian sea bass (Lates calcarifer) and one with Japanese sea bass (Lateolabrax japonicus). Temperature was logged hourly and summarized as daily means; water quality was sampled biweekly; fish were measured repeatedly, with endpoint growth compared within species. The result shows that shading lowered pond temperature and the diurnal temperature range and reduced the number of days above species benchmark temperatures. Indicators associated with phytoplankton, including suspended solids and chlorophyll a, were lower under shading, whereas dissolved inorganic nutrients were higher. In the Japanese sea bass trial, dissolved oxygen was higher without shading. Final body weight did not differ between treatments within either trial, but survival was higher with 40% shading. Principal component analysis and permutational multivariate analysis of variance indicated a treatment signal in multivariate water quality. Because the trials occurred in different years with one pond per treatment, inference was restricted to contrasts within each species. Overall, moderate surface shading cooled ponds and altered water quality without reducing growth.
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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 | 0.000 |
| 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) | 0.001 | 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".