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Record W4392155326 · doi:10.3390/plants13050623

Correction: Haegeman et al. Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests. Plants 2023, 12, 2139

2024· erratum· en· W4392155326 on OpenAlexaff
Annelies Haegeman, Yoika Foucart, Kris De Jonghe, Thomas Goedefroit, Maher Al Rwahnih, Neil Boonham, Thierry Candresse, Yahya Z. A. Gaafar, Oscar P. Hurtado‐Gonzales, Zala Kogej Zwitter, Denis Kutnjak, Janja Lamovšek, Marie Lefebvre, Martha Malapi‐Wight, Irena Mavrič Pleško, Serkan Önder, Jean-Sébastien Reynard, Ferran Salavert Pamblanco, Olivier Schumpp, Kristian Stevens, Chandan Pal, Lucie Tamisier, Çiğdem Ulubaş Serçe, Inge van Duivenbode, David W. Waite, Xiaojun Hu, Heiko Ziebell, Sébastien Massart

Bibliographic record

VenuePlants · 2024
Typeerratum
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsBiologyComputational biologyComputer scienceVirology

Abstract

fetched live from OpenAlex

In the original publication [...].

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)0.009
metaresearch head score (Gemma)0.120
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 score0.047
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.120
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.003
Science and technology studies0.0050.004
Scholarly communication0.0060.003
Open science0.0050.003
Research integrity0.0100.018
Insufficient payload (model declined to judge)0.0470.036

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 head0.120
GPT teacher head0.310
Teacher spread0.189 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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