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Record W2063395881 · doi:10.1111/jfd.12022

Detection of emamectin benzoate tolerance emergence in different life stages of sea lice, <i><scp>L</scp>epeophtheirus salmonis</i>, on farmed <scp>A</scp>tlantic salmon, <i><scp>S</scp>almo salar </i><scp>L</scp>

2013· article· en· W2063395881 on OpenAlexafffundabout
Patti Jones, K. Larry Hammell, G. Gettinby, Crawford W. Revie

Bibliographic record

VenueJournal of Fish Diseases · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsUniversity of Prince Edward Island
FundersNova Scotia Department of Fisheries and Aquaculture
KeywordsBiologyEmamectin benzoateLepeophtheirusPopulationZoologySalmoAbundance (ecology)LarvaToxicologyFisheryEcologyPesticideFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

Emamectin benzoate has been used to treat sea lice, Lepeophtheirus salmonis, infestations on farmed Atlantic salmon, Salmo salar. Recent evidence suggests a reduction in effectiveness in some locations. A major challenge in the detection of tolerance emergence can be the typically low proportion of resistant individuals in a population during the early phases. The objectives of this study were to develop a method for determining differences in temporal development of tolerance between sea lice life stages and to explore how these differences might be used to improve the monitoring of treatment effectiveness in a clinical setting. This study examined two data sets based on records of sea lice abundance following emamectin benzoate treatments from the west coast of Scotland (2002-2006) and from New Brunswick, Canada (2004-2008). Life stages were categorized into two groups (adult females and the remaining mobile stages) to examine the trends in mean abundance and treatment effectiveness. Differences in emamectin benzoate effectiveness were found between the two groups by year and location, suggesting that an important part of monitoring drug resistance development in aquatic ectoparasites may be the need to focus on key life stages.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.262
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations74
Published2013
Admission routes3
Has abstractyes

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