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Record W2557041881 · doi:10.1111/jfd.12569

Evaluating bath treatment effectiveness in the control of sea lice burdens on Atlantic salmon in New Brunswick, Canada

2016· article· en· W2557041881 on OpenAlexafffundabout
Roshani Gautam, Raphaël Vanderstichel, Annette S. Boerlage, Crawford W. Revie, K. Larry Hammell

Bibliographic record

VenueJournal of Fish Diseases · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsOttawa HospitalUniversity of Prince Edward IslandUniversity of Ottawa
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsBiologyFisheryTreatment modalityToxicologyModalitiesFish <Actinopterygii>Animal scienceMedicineSurgery

Abstract

fetched live from OpenAlex

Abstract The use of medicinal bath treatment for sea lice is becoming more common, due to increasing resistance to in‐feed treatments with emamectin benzoate. Common treatment modalities in New Brunswick, Canada, include Salmosan administered by tarpaulin or wellboat, and Paramove administered by wellboat. In this study, we assessed the effectiveness of these treatment modalities in the field between 2010 and 2015 using a web‐based sea lice data management system (Fish‐iTrends © ). Effectiveness was evaluated for adult female (AF) and for pre‐adult and adult male (PAAM) life stages separately. We also investigated the impact of variability in pretreatment lead and post‐treatment lag time on effectiveness measures. There were 1185 treatment events at 57 farms that uniquely matched our pre‐ and post‐treatment count criteria. The effectiveness of treatment modality was significantly influenced by season, pretreatment level of sea lice and by lead and lag times. In summer, Salmosan administered by tarpaulin had the greatest effectiveness on both AF and PAAM, when pretreatment levels were above 10 sea lice; whereas in autumn, the performance of treatment modalities varied significantly, depending on the pretreatment levels for the life stages. Ignoring the lead or lag time effect generally resulted in an underestimation of treatment effectiveness.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.327
Teacher spread0.308 · 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

Citations12
Published2016
Admission routes3
Has abstractyes

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