Bibliographic record
Abstract
Aquaculture in the Russian Federation is an integral part of the agricultural industry of the state economy. Countries with high rates of aquaculture growth (Norway, USA, China, Japan, Canada, etc.) and increasing efficiency of fish farming are the cradles of infectious diseases, which, in case of improper control, invade the territory of other countries and spread to new areas, bearing the risks for the domestic industry too. In recent years, infectious hematopoietic necrosis (IHN) has caused significant damage to fish farms. In 2020, Estonia suffered heavy losses; more than 65 tons of rainbow trout died and were destroyed during the IHN outbreak with a mortality rate of 71%. This was the first IHN case in this country. The aggravation of the epidemic situation at Estonian fish farms poses a threat to the northwestern regions of the Russian Federation, where aquaculture is practiced (the Leningrad Oblast and the Republic of Karelia). In 2022, IHN outbreaks were reported in France, Italy, Finland, Germany, Denmark and Macedonia. IHN-caused deaths were reported at the river trout farm in Georgia in 2023 for the first time. The domestic aquaculture depends on the import of eggs and seed material from Norway, Denmark, Finland and other countries, therefore a regular disease monitoring is urgently needed. The paper provides a brief description of the IHN causative agent, describes its epidemiology, pathogenesis, clinical signs, post-mortem lesions, diagnostic tests, infection control and prevention measures. We have reviewed 88 literature sources to summarize the information.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".