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Record W2587153618 · doi:10.1111/jfd.12607

A survey of microparasites present in adult migrating Chinook salmon (<i>Oncorhynchus tshawytscha</i>) in south‐western British Columbia determined by high‐throughput quantitative polymerase chain reaction

2017· article· en· W2587153618 on OpenAlexafffundabout
Arthur L. Bass, S. G. Hinch, Amy K. Teffer, David A. Patterson, Kristina M. Miller

Bibliographic record

VenueJournal of Fish Diseases · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsFisheries and Oceans CanadaSimon Fraser UniversityUniversity of VictoriaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsChinook windOncorhynchusBiologyFisheryPolymerase chain reactionThroughputFish <Actinopterygii>GeneticsGene

Abstract

fetched live from OpenAlex

Microparasites play an important role in the demography, ecology and evolution of Pacific salmonids. As salmon stocks continue to decline and the impacts of global climate change on fish populations become apparent, a greater understanding of microparasites in wild salmon populations is warranted. We used high-throughput, quantitative PCR (HT-qRT-PCR) to rapidly screen 82 adult Chinook salmon from five geographically or genetically distinct groups (mostly returning to tributaries of the Fraser River) for 45 microparasite taxa. We detected 20 microparasite species, four of which have not previously been documented in Chinook salmon, and four of which have not been previously detected in any salmonids in the Fraser River. Comparisons of microparasite load to blood plasma variables revealed some positive associations between Flavobacterium psychrophilum, Cryptobia salmositica and Ceratonova shasta and physiological indices suggestive of morbidity. We include a comparison of our findings for each microparasite taxa with previous knowledge of its distribution in British Columbia.

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)0.001
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 score0.277
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.309
Teacher spread0.293 · 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

Citations49
Published2017
Admission routes3
Has abstractyes

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