Wild juvenile salmonids in Muchalat Inlet, British Columbia, Canada: factors associated with sea lice prevalence
Bibliographic record
Abstract
The Muchalat Inlet, British Columbia, is among the most westerly points at which aquaculture is practiced in Canada. In this paper, we summarise data from over 18000 wild fish sampled at 16 sites over an 8 yr period, between 2004 and 2011. The most prevalent wild species was chum salmon Oncorhynchus keta (82.4%), followed by Chinook O. tshawytscha (10%) and coho O. kisutch (4.3%). However, inter-annual and seasonal variation was evident, and smaller numbers of other Pacific salmon and stickleback species were sporadically observed. A high percentage of wild salmon (~95%) had no sea lice parasites present, with less than 1% of the fish hosting a mobile-stage sea louse. Of the data for which sea lice species were recorded, just over 96% of samples were identified as Lepeophtheirus salmonis. Logistic regression models assessed the association between the presence of lice and a range of independent variables. These models indicated a significant degree of spatial variation, much of which could be explained in terms of salinity levels. There were also important variations through time, both over the season within a year and across years. In addition, coho salmon were significantly more likely (odds ratio = 1.65; 95% CI = 1.20-2.3) to be infected than chum salmon. The protective effect of low salinity was most clearly seen at values lower than 15 psu, although this was dependent on fish species.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".