Preventing and mitigating farmed bivalve disease: a Northern Ireland case study
Bibliographic record
Abstract
Abstract Shellfish production forms a large proportion of marine aquaculture production in Northern Ireland (NI). Diseases represent a serious threat to the maintenance and growth of shellfish cultivation with severe consequences to production output and profitability. In Northern Ireland, production generally benefits from a good health status with the absence of notifiable diseases, except for localised cases of Bonamia ostreae , Marteilia refringens and ostreid herpes virus. In this paper, we qualitatively explore that the prevalence, risk, impact, mitigation and experience shellfish farmers in this region have in relation to disease. Sixteen semi-structured interviews were conducted with stakeholders within the sector. The interviews were transcribed verbatim, and Nvivo 12 was used to facilitate an inductive thematic analysis. Our results highlighted that the industry has varying attitudes and experiences with disease. At present-day temperatures, disease is not an issue and this provides vast market opportunities for the region. However, disease outbreaks have led to detrimental consequences to financial income, production output and reputation in the past, whilst control and mitigation remain reactive. It is imperative proactive disease prevention and control that are employed and enforced to sustain NI’s reputation as a healthy shellfish region, particularly under increasing global temperatures and intensified production systems. A cultural shift to disease appreciation, risk analysis and surveillance through research, education, training and collaboration is essential. This study highlights the importance of providing a bottom-up communication platform with the stakeholders directly involved in shellfish culture and management, the value of cross sector engagement and the need to improve knowledge transfer between science the sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".