What are the effects of sea lice on wild and farmed Pacific and Atlantic salmon? A systematic map protocol
Bibliographic record
Abstract
Abstract Wild Pacific salmon are a culturally, ecologically and economically important group of fishes. Unfortunately, several distinct lineages have already been lost. The abundance of these species varies over interdecadal and centennial time scales due to climatic and local ecosystem complexity and dynamics. Sea lice from net‐pen salmon farms (traditional flow‐through containment systems) have been linked to negative effects on wild salmon. However, there is debate over the quality of evidence for these claims. Fisheries and Oceans Canada Aquaculture Directorate requested a knowledge synthesis on the impact of sea lice from net‐pen salmon farms on wild Pacific salmon in British Columbia. To ensure a full understanding of this host–parasite system, we propose to first compile a global inventory of studies investigating the effects of sea lice (in the genera Lepeophtheirus or Caligus ) on wild, enhanced or farmed Pacific salmon (Chinook salmon Oncorhynchus tshawytscha , coho salmon O. kisutch , chum salmon O. keta , sockeye salmon O. nerka or pink salmon O. gorbuscha ) or Atlantic salmon ( Salmo salar ), with any outcome related to adult abundance, reproduction or productivity; outcomes at any life stage related to performance, population‐level survival or sea lice infestation levels; measures of species‐level differences in susceptibility to lice; or how environmental factors (e.g. salinity, temperature, currents) affect sea lice. This systematic map will capture evidence available in published and grey literature. We will search for and identify relevant literature using bibliographic databases, search engines, specialist websites and databases and networking tools. Eligibility screening will be conducted at two stages: (1) title and abstract and (2) full text. Relevant information from included papers will be coded and entered into a database. Practical implications . We will use a narrative synthesis and descriptive statistics to describe key characteristics of the evidence base (e.g. number of publications, focal salmon and lice species, life stage of salmon and lice, study objectives, exposure and comparator details, outcomes and study designs). We will identify knowledge gaps to inform future research needs and subtopics (evidence clusters) that are sufficiently covered to allow for a full systematic review from visual heat maps.
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 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) | 0.000 | 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".