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Analysis of the influence factors of coffee on human life

2023· article· en· W4400835623 on OpenAlexaff
Tianxiao Liu

Bibliographic record

VenueTheoretical and Natural Science · 2023
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFood sciencePsychologyChemistry

Abstract

fetched live from OpenAlex

Coffee, as one of the world's most widely consumed beverages, has long held a prominent place in both daily enjoyment and scientific scrutiny. This study aims to comprehensively explore the multifaceted impact of coffee on the human body, with a particular emphasis on its influence on attention, the prevention of chronic diseases, and the potential associated risks. This article conducts a thorough analysis of coffee's primary constituents, encompassing caffeine and antioxidant compounds, and conducts an exhaustive review of pertinent research studies. The research findings unequivocally highlight that moderate coffee consumption can substantially enhance attention and significantly mitigate the risk of chronic diseases. However, it is imperative to acknowledge that excessive coffee intake may precipitate issues such as insomnia and gastrointestinal discomfort. Thus, this study underscores, with utmost clarity, the pivotal significance of temperate coffee consumption, laying a robust foundation for future investigations into the intricate interplay between coffee and human health.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.365
Teacher spread0.341 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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