Quantifying key parameters related to the life cycle of <i>Caligus rogercresseyi</i>
Bibliographic record
Abstract
The salmon louse Caligus rogercresseyi (Boxshall and Bravo 2000) is a common ectoparasite of farmed salmonids in Chile. Sea lice can negatively impact the growth of hosts, adversely affecting aquaculture productivity. Unlike Lepeophtheirus salmonis (Krøyer, 1838), whose life cycle parameters have been well studied due to its importance in the Northern Hemisphere, for C. rogercresseyi no single source exists that quantifies the parameters required to model this ectoparasite's life cycle. Given that different species of sea lice have substantially different biological characteristics, it is important to parameterize the life cycle of C. rogercresseyi using appropriate observational data, rather than simply trying to adapt parameters developed for L. salmonis. Using data from existing literature, we quantified the development and survival rates for each stage in the C. rogercresseyi life cycle. We illustrate how development rates are affected by water temperature and explore the important impacts of salinity on rates of survival. We present equations that can be used to model development periods and survival proportions given certain water temperature and salinity profiles. While key parameters to quantitatively model the life cycle of C. rogercresseyi are presented, further research is required to adequately model the complete population dynamics of this ectoparasite on Chilean salmon farms and consequently to support decision-making to achieve effective control and mitigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".