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Record W2982622399 · doi:10.3389/fphys.2019.01354

Corrigendum: High-Load Reovirus Infections Do Not Imply Physiological Impairment in Salmon

2019· erratum· en· W2982622399 on OpenAlexaffabout
Yangfan Zhang, Mark P. Polinski, Phillip R. Morrison, Colin J. Brauner, Anthony P. Farrell, Kyle A. Garver

Bibliographic record

VenueFrontiers in Physiology · 2019
Typeerratum
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaUniversity of British Columbia
Fundersnot available
KeywordsFisheryFish <Actinopterygii>AquacultureStatement (logic)State (computer science)Operations researchComputer sciencePolitical scienceBiologyEngineeringLawAlgorithm

Abstract

fetched live from OpenAlex

Corrigendum on: Zhang Y, Polinski MP, Morrison PR, Brauner CJ, Farrell AP and Garver KA (2019)High-Load Reovirus Infections Do Not Imply Physiological Impairment in Salmon. Front. Physiol. 10:114. doi: 10.3389/fphys.2019.00114There is an error in the Funding statement. The correct number for ** Aquaculture Collaborative Research and Development Program within Fisheries and Oceans Canada** is **16-1-P-03**. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.In the original article, we neglected to include that the funder **Aquaculture Collaborative Research and Development Program within Fisheries and Oceans Canada**, **16-1-P-03** to **KG** involved collaborative support from **The British Columbia Salmon Farmers Association**. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. Text has been added to specify that **Collaborative support in this instance was provided by from the 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 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)0.003
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.091
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0910.066

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 head0.224
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2019
Admission routes2
Has abstractyes

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