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Record W2768611981 · doi:10.1111/jfd.12692

Horizontal and vertical distribution of sea lice larvae (<i>Lepeophtheirus salmonis</i>) in and around salmon farms in the Bay of Fundy, Canada

2017· article· en· W2768611981 on OpenAlexaffabout
Elizabeth Nelson, Shawn Robinson, Nathaniel Feindel, Andrea M. Sterling, Aidan Byrne, Keng Pee Ang

Bibliographic record

VenueJournal of Fish Diseases · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsCooke Aquaculture (Canada)University of VictoriaPositive Living NorthFisheries and Oceans Canada
Fundersnot available
KeywordsLepeophtheirusBiologyFisheryBayLarvaDiel vertical migrationPlanktonPopulationEcologyAquacultureOceanographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract The sea louse, Lepeophtheirus salmonis , is parasitic to salmonid species in the Northern Hemisphere and has become a widespread biological and economic problem for the salmon farming industry. A better understanding is needed of their spatial distribution and early life history to disrupt the life cycle of the sea louse. In this study, sea lice larval densities within salmon farms, between salmon farms and reference sites, and at various depths were quantified using both plankton pumps and plankton nets. Farm sites exhibited significantly higher densities than reference sites; however, these densities dropped an order of magnitude at a distance of 100 m from the cages. The majority of the larvae captured in the study were nauplii (93%), and densities ranged from 0 to 10 larvae/m 3 . Free‐swimming sea lice larvae were found to exhibit a diel cycle where nauplii larvae were in deeper waters (10–17 m) during the day and in surface waters (1–6 m) during the night. The results of this study suggest that the early life‐history stages of sea lice originate from and may remain close to active salmon farms, creating a self‐sustaining population.

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 score0.223
Threshold uncertainty score0.955

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 head0.011
GPT teacher head0.278
Teacher spread0.267 · 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

Citations31
Published2017
Admission routes2
Has abstractyes

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