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Record W2187861494

New Approaches for Controlling Saprolegnia parasitica, the Causal Agent of a Devastating Fish Disease.

2014· article· en· W2187861494 on OpenAlexaff
Gregory Earle, William E. Hintz

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsOomyceteMalachite greenBiologySaprolegniaFish <Actinopterygii>PathogenMicrobiologyFisheryChemistry
DOInot available

Abstract

fetched live from OpenAlex

Pathogenic oomycetes have the ability to infect a wide range of plant and animal hosts and are responsible for a number of economically important diseases. Saprolegniasis, a disease affecting fish eggs and juvenile fish in hatcheries worldwide, is caused by the pathogenic oomycete Saprolegnia parasitica. This disease presents as greyish-white patches of filamentous mycelium on the body or fins of fish and is associated with tissue damage leading to death of the animal. Traditionally, saprolegniasis was controlled using Malachite green; however, this chemical was banned in 2002 due to its carcinogenic and toxicological effects. As a direct result of this ban, there has been a recent resurgence of saprolegniasis in the aquaculture industry, leading to economic losses world-wide. Hence, there is an urgent need to find alternative methods to control this pathogen. We discuss the use of molecular approaches for the study of saprolegniasis, which are anticipated to enable the development of effective fish vaccines and the potential for the development of new methods to control this devastating disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.216
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations34
Published2014
Admission routes1
Has abstractyes

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