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Record W3011739768 · doi:10.1038/srep46124

Correction: Corrigendum: Ectopic expression of Arabidopsis Target of Rapamycin (AtTOR) improves water-use efficiency and yield potential in rice

2017· erratum· en· W3011739768 on OpenAlexaff
Achala Bakshi, Mazahar Moin, Manish Kumar, Aramati B. M. Reddy, Maozhi Ren, Raju Datla, E. A. Siddiq, Pulugurtha Bharadwaja Kirti

Bibliographic record

VenueScientific Reports · 2017
Typeerratum
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsPlant Biotechnology Institute
Fundersnot available
KeywordsEctopic expressionBeijingArabidopsisYield (engineering)Library scienceComputational biologyBiologyBiotechnologyComputer sciencePolitical scienceChinaGeneticsGeneLaw

Abstract

fetched live from OpenAlex

Scientific Reports 7: Article number: 42835; published online: 23 February 2017; updated: 10 April 2017 This Article contains errors in the Affiliations of Maozhi Ren and Raju Datla. The correct affiliations for these authors are listed below: Maozhi Ren Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, P.

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)0.028
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 score0.042
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.028
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0420.025

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 head0.213
Teacher spread0.197 · 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

Citations109
Published2017
Admission routes1
Has abstractyes

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