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SELECTION OF AN ASTRINGENCY REFERENCE STANDARD FOR THE SENSORY EVALUATION OF BLACK TEA

2004· article· en· W2154777645 on OpenAlexafffund
Zuzana Drobná, Wendy V. Wismer, Laksiri A. Goonewardene

Bibliographic record

VenueJournal of Sensory Studies · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsAstringentTannic acidBlack teaTasteChemistryFood sciencePsychologyBitter tasteAlumOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT Astringent and bitter sensations are characteristic sensory qualities of black tea. Three different classes of potential astringent reference standards (two concentrations each of alum and tannic acid and three fruit juices) were evaluated in this study. The perceived astringency, bitterness and sourness of each were profiled using computerized time‐intensity and compared with the astringent intensity of a standardized brew of black tea. The differences in temporal profiles of potential reference standards across taste attributes were evident and intensity ratings were found to be dependent upon the stimulus and its concentration. Both concentrations of tannic acid were evaluated as the highest in perceived bitterness. For the juices, a strong sour taste was perceived in addition to astringency. It was concluded that the best reference standard for the astringency of black tea is a solution of 0.7 g/L alum as it is low in perceived bitterness and sourness.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.259
GPT teacher head0.415
Teacher spread0.156 · 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 designBench or experimental
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

Citations32
Published2004
Admission routes2
Has abstractyes

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