Healthy Culture of Aquatic Animals and Development of Green Fishery Medicine
Bibliographic record
Abstract
Since reforming and opening to the outside world, Chinese aquiculture has developed very quickly. The total output ofaquiculture has been ranking first in the world over ten years, but it still has many problems. In this article, manyproblems are listed about Chinese aquiculture, which has seriously influenced the quality of aquatic products anddestroyed the whole aquiculture ecological environment, so it is imperative under the situation to implement healthyculture. At the same time, the actuality of Chinese healthy culture is analyzed, and healthy culture has been developedin freshwater and sea water. Healthy culture comes down to many aspects, and it is a very important part to reasonablyuse fishery medicines. The using of fishery medicines should start from many aspects including medicine materials,cause of disease, environment, aquatic animals, and human health, and only to use medicines intentionally andeffectively can achieve the effect of preventing and treating diseases. The green fishery medicine is one most effectivedevelopment direction at present to use medicines reasonably. Green fishery medicines include fishery vaccine, Chineseherbal medicine preparation, animalcule preparation, and biologic fishery medicines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".