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Factors associated with the incidence of bacterial gill disease in salmonid lots reared in Ontario, Canada government hatcheries

2007· article· en· W1988165147 on OpenAlexafffundabout
Christopher Good, Margaret A. Thorburn, Roselynn M. W. Stevenson

Bibliographic record

VenuePreventive Veterinary Medicine · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of Guelph
FundersMinistry of Natural Resources
KeywordsHatcheryTroutFisheryIncidence (geometry)PopulationRainbow troutBiologyOutbreakFish hatcheryDemographyVeterinary medicineFish <Actinopterygii>Environmental healthMedicineFish farmingAquaculture

Abstract

fetched live from OpenAlex

Bacterial gill disease (BGD) (causative agent: Flavobacterium branchiophilum) has been a persistent problem in early-rearing salmonids in the Ontario Ministry of Natural Resources (OMNR) fish hatchery system. Retrospective epidemiological investigations of BGD diagnoses and treatments in OMNR fish hatcheries during the period 1991-2001 were conducted using University of Guelph Fish Health Laboratory and OMNR central office data. All investigations were conducted at the lot-level, which is the major within-hatchery-level of population aggregation. Survivorship of BGD diagnosis in early-rearing lots within seven individual hatcheries ranged from 84.2 to 100%; within individual species groups, survivorship was lowest (84.6%) in brook trout (Salvelinus fontinalis) lots. Annual risk percentages (cumulative incidence) for BGD diagnosis within hatchery and species groups varied considerably among years. Multivariable proportional hazards survival analysis indicated that the species brook trout, and the Spring (March-May) season, were significantly associated with treatment for BGD. Combined, these results emphasize the importance of hatchery, species, and time on the development of BGD. Future observational research on this disease must consider these factors in their design and analysis.

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 score0.487
Threshold uncertainty score0.773

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)0.0010.000

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 head0.023
GPT teacher head0.234
Teacher spread0.211 · 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

Citations16
Published2007
Admission routes3
Has abstractyes

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