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Tea and Cancer Prevention

2015· article· en· W622625676 on OpenAlex
Xingcai Zhang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of cancer research updates · 2015
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsCancer preventionHarmony (color)Human healthCancerHealth benefitsMedicinePolyphenolLife styleTraditional medicineMechanism (biology)DiseaseEnvironmental healthChemistryBiochemistryInternal medicineArt

Abstract

fetched live from OpenAlex

Cancer remains one of the biggest challengesin the 21st century, therefore anti-cancer drugs and their delivery systems are under developing for better treatment. Tea is the amazing gift nature offered to us with great health benefits. Tea polyphenols especially EGCG and Theoflavins have widely been studied and expected to be a very promising nature polyphenol for the prevention of cancer, cardiology disease, aging, weight control etc. Here“Dr. Tea summarized the past studies about tea and cancer prevention, through the chemical composition, structure, epidemiologic study and mechanism analysis. And based on the epidemiologic study results, a layer-by-layer multi-functional drug delivery system and synergy studies based on our past scientific working experience had been proposed for future tea and cancer research. A Healthy, Harmony, Pure & Nature tea-style of living is proposed for all human-beings towards a better living self and a better society.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

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

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 head0.145
GPT teacher head0.500
Teacher spread0.355 · 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