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Record W2094642926 · doi:10.3354/dao070101

Randomized clinical trial to investigate the effectiveness of teflubenzuron for treating sea lice on Atlantic salmon

2006· article· en· W2094642926 on OpenAlexaffabout
PJ Campbell, K. Larry Hammell, IR Dohoo, Gordon Ritchie

Bibliographic record

VenueDiseases of Aquatic Organisms · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsLepeophtheirusBiologySalmoCageFisheryAquacultureVeterinary medicineAnimal scienceToxicologyFish <Actinopterygii>Medicine

Abstract

fetched live from OpenAlex

A double-blind, randomized control clinical trial was performed to investigate the effectiveness of teflubenzuron in controlling sea lice Lepeophtheirus salmonis on farmed Atlantic salmon Salmo salar. A total of 40 sea cages from 3 commercial cage sites in Atlantic Canada were used in this Good Clinical Practice (GCP) trial. The teflubenzuron was administered in the feed at a dosage of 10 mg kg(-1) biomass d(-1) for 7 d. Medicated and control cages were matched by site, cage size, and pre-treatment mean lice counts using cages as the unit of concern. Post-treatment lice counts and staging of developmental stages were performed at 1 and 2 wk after the end of treatment. Chalimus stages in medicated cages were significantly lower than in control cages at 1 wk (79% reduction in mean lice counts, p < 0.001), and at 2 wk (53% reduction, p < 0.001). Mobile (pre-adult and adult) stages were also significantly reduced in medicated cages at 1 wk (69% reduction, p < 0.01), and at 2 wk (40% reduction, p < 0.01) post-treatment, respectively. Teflubenzuron was proven effective for reducing lice burdens on salmon despite the low parasite levels experienced during the trial and the recruitment of lice from the untreated cages. The use of cage as the unit of concern was an important design component of this trial.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.352
Teacher spread0.331 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations22
Published2006
Admission routes2
Has abstractyes

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