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Record W2980981279 · doi:10.1039/c8np90041h

Correction: The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research

2018· erratum· en· W2980981279 on OpenAlexaff
James B. McAlpine, Shao‐Nong Chen, Andrei G. Kutateladze, John B. MacMillan, Giovanni Appendino, Andersson Barison, Mehdi A. Beniddir, Maique W. Biavatti, Stefan Blüml, Asmaa Boufridi, Mark S. Butler, Robert J. Capon, Young Hae Choi, David Coppage, Phillip Crews, Michael T. Crimmins, Marie Csete, Pradeep Dewapriya, Joseph M. Egan, Mary J. Garson, Grégory Genta‐Jouve, William H. Gerwick, Harald Gross, Mary Kay Harper, Precilia Hermanto, James M. Hook, Luke Hunter, Damien Jeannerat, Nai‐Yun Ji, Tyler A. Johnson, David G. I. Kingston, Hiroyuki Koshino, Hsiau‐Wei Lee, Guy Lewin, Jie Li, Roger G. Linington, Miaomiao Liu, Kerry L. McPhail, Tadeusz F. Molinski, Bradley S. Moore, Joo‐Won Nam, Ram P. Neupane, Matthias Niemitz, Jean‐Marc Nuzillard, Nicholas H. Oberlies, Fernanda Maria Marins Ocampos, Guohui Pan, Ronald J. Quinn, D. Sai Reddy, Jean‐Hugues Renault, José Rivera‐Chávez, Wolfgang Robien, Carla M. Saunders, Thomas J. Schmidt, Christoph Seger, Ben Shen, Christoph Steinbeck, Hermann Stuppner, Sonja Sturm, Orazio Taglialatela‐Scafati, Dean J. Tantillo, Robert Verpoorte, Bin‐Gui Wang, Craig M. Williams, Philip G. Williams, Julien Wist, Jian‐Min Yue, Chen Zhang, Zhengren Xu, Charlotte Simmler, David C. Lankin, Jonathan Bisson, Guido F. Pauli

Bibliographic record

VenueNatural Product Reports · 2018
Typeerratum
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsBurnaby HospitalSimon Fraser University
FundersNational Center for Complementary and Integrative Health
KeywordsTransparency (behavior)Raw dataReproducibilityNatural productResearch integrityComputer scienceChemistryEngineeringStereochemistryEngineering ethicsChromatographyComputer security

Abstract

fetched live from OpenAlex

Correction for ‘The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research’ by James B. McAlpine et al. , Nat. Prod. Rep. , 2018, DOI: 10.1039/c7np00064b.

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)0.008
metaresearch head score (Gemma)0.143
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.143
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0060.005
Scholarly communication0.0080.004
Open science0.0050.005
Research integrity0.0090.017
Insufficient payload (model declined to judge)0.0970.081

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 head0.061
GPT teacher head0.348
Teacher spread0.287 · 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.

Study designNot applicable
DomainReproducibility
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

Citations5
Published2018
Admission routes1
Has abstractyes

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