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Record W2955542252 · doi:10.1111/nph.15976

Misdiagnosis and uncritical use of plant mycorrhizal data are not the only elephants in the room

2019· letter· en· W2955542252 on OpenAlexafffund
C. Guillermo Bueno, Laura Aldrich‐Wolfe, V. Bala Chaudhary, Maret Gerz, Thorunn Helgason, Jason D. Hoeksema, John N. Klironomos, Ylva Lekberg, Daniela León, Hafiz Maherali, Maarja Öpik, Martin Zobel, Mari Moora

Bibliographic record

VenueNew Phytologist · 2019
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of GuelphUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaEuropean CommissionSight Research UKEuropean Regional Development FundMax-Planck-GesellschaftOffice of Experimental Program to Stimulate Competitive ResearchNational Science FoundationMPG Ranch
KeywordsBiologyEnvironmental sciencePsychology

Abstract

fetched live from OpenAlex

Misdiagnosis and uncritical use of plant mycorrhizal data are not the only elephants in the room: A response to Brundrett & Tedersoo (2018) 'Misdiagnosis of mycorrhizas and inappropriate recycling of data can lead to false conclusions'.

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)0.138
metaresearch head score (Gemma)0.380
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.862
Threshold uncertainty score0.732

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1380.380
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0180.011
Science and technology studies0.0030.021
Scholarly communication0.0130.024
Open science0.0070.011
Research integrity0.0050.012
Insufficient payload (model declined to judge)0.0050.006

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 head0.083
GPT teacher head0.259
Teacher spread0.176 · 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
DomainMethods
GenreCommentary

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

Citations40
Published2019
Admission routes2
Has abstractyes

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