Bibliographic record
Abstract
The global aquaculture industry is experiencing rapid growth, with a significant contribution to food security worldwide. A significant challenge to this is the increase of emerging and re-emerging diseases in recent years that result in significant economic losses for various global aquaculture industries. To mitigate the negative impact of these diseases, rapid discovery, characterisation and diagnosis of causative agents and risk factors is crucial. It is also essential to adopt a multi-disciplinary approach to the investigation and control of emerging diseases. This special issue aimed to collect high-quality work focused on the discovery, diagnosis and characterisation of emerging diseases, as well as topical reviews on emerging diseases of farmed aquatic species. The special issue we are presenting here covers a broad spectrum of emerging infectious diseases, including viral, bacterial, fungal and parasitic pathogens from diverse geographical locations. The range of host species covered is wide, and this issue includes studies that provide critical insights into pathogen threats affecting crustaceans, finfish and molluscs. This is achieved through 16 peer-reviewed publications, including 10 clinical case studies, six pathogenesis and/or pathogen characterisation studies, one diagnostic method study and one literature review. We provide a brief summary of the topics covered below.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.100 | 0.070 |
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".