A gap analysis on modelling of sea lice infection pressure from salmonid farms. II. Identifying and ranking knowledge gaps: output of an international workshop
Bibliographic record
Abstract
Sea lice are a major health hazard for farmed Atlantic salmon in Europe, and their impact is felt globally. Given the breadth of ongoing research in sea lice dispersal and population modelling, and focus on research-led adaptive management, we brought experts together to discuss research knowledge gaps. Gaps for salmon lice infection pressure from fish farms were identified and scored by experts in sea lice-aquaculture-environment interactions, at an international workshop in 2021. The contributors included experts based in Scotland, Norway, Ireland, Iceland, Canada, the Faroe Islands, England and Australia, employed by governments, industry, universities and non-government organisations. The workshop focused on knowledge gaps underpinning 5 key stages in salmon lice infection pressure from fish farms: larval production; larval transport and survival; exposure and infestation of new hosts; development and survival of the attached stages; and impact on host populations. A total of 47 research gaps were identified; 5 broad themes emerged with 13 priority research gaps highlighted as important across multiple sectors. The highest-ranking gap called for higher quality and frequency of on-farm lice count data, along with better sharing of information across sectors. We highlight the need for synergistic international collaboration to maximise transferable knowledge. Round table discussions through collaborative workshops provide an important forum for experts to discuss and agree research priorities.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".