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Record W2783195410 · doi:10.1111/all.13383

Editorial: With tree nut sensitization, take the current when it serves, or lose our ventures

2018· editorial· en· W2783195410 on OpenAlexaboutno aff
Matthew Greenhawt

Bibliographic record

VenueAllergy · 2018
Typeeditorial
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsnot available
FundersAgency for Healthcare Research and Quality
KeywordsNutTree (set theory)ConfusionMedicineSensitizationAllergyIdentification (biology)PsychologyImmunologyMathematicsBiology

Abstract

fetched live from OpenAlex

With tree nut sensitization, take the current when it serves, or lose our ventures Tree nut allergy affects approximately 1.4%-2.3%,depending on the study methodology, patient age, and region in which the study was conducted.1,2 Tree nut allergy can be severe and tends to persist

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)0.023
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.043
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0030.001
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0430.039

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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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
GenreEditorial

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

Citations2
Published2018
Admission routes1
Has abstractyes

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