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Caffeine: Friend or Foe?

2016· review· en· W1952911830 on OpenAlexaff
Candace Doepker, Harris R. Lieberman, Andrew Smith, Jennifer D. Peck, Ahmed El‐Sohemy, Brian T. Welsh

Bibliographic record

VenueAnnual Review of Food Science and Technology · 2016
Typereview
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSession (web analytics)CaffeinePresentation (obstetrics)Key (lock)PsychologyPublic relationsMedicineEngineering ethicsPolitical scienceBusinessComputer scienceEngineeringComputer securityPsychiatryAdvertising

Abstract

fetched live from OpenAlex

The debate on the safety of and regulatory approaches for caffeine continues among various stakeholders and regulatory authorities. This decision-making process comes with significant challenges, particularly when considering the complexities of the available scientific data, making the formulation of clear science-based regulatory guidance more difficult. To allow for discussions of a number of key issues, the North American Branch of the International Life Sciences Institute (ILSI) convened a panel of subject matter experts for a caffeine-focused session entitled "Caffeine: Friend or Foe?," which was held during the 2015 ILSI Annual Meeting. The panelists' expertise covered topics ranging from the natural occurrence of caffeine in plants and interindividual metabolism of caffeine in humans to specific behavioral, reproductive, and cardiovascular effects related to caffeine consumption. Each presentation highlighted the potential risks, benefits, and challenges that inform whether caffeine exposure warrants concern. This paper aims to summarize the key topics discussed during the session.

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)0.005
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.050
GPT teacher head0.425
Teacher spread0.375 · 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
GenreReview

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

Citations85
Published2016
Admission routes1
Has abstractyes

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