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Record W1968168018 · doi:10.5539/jfr.v1n1p179

Sensory Analysis of Pawpaw (Asimina triloba) Pulp Puree: Consumer Appraisal and Descriptive Lexicon

2012· article· en· W1968168018 on OpenAlex

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 Food Research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPulp (tooth)Sensory analysisFlavorSensory systemFood scienceLexiconChemistryMathematicsBiologyComputer scienceMedicineArtificial intelligenceDentistry

Abstract

fetched live from OpenAlex

Consumer and descriptive sensory analysis was performed on pawpaw pulp. Consumer sensory analysis showed that mango was preferred compared to the pawpaw, but that only one-third of those who preferred the mango were correctly able to identify it. Consumers generated 25 flavor descriptors for pawpaw pulp, with banana and mango being the most identified. Descriptive sensory analysis was performed on pawpaw pulp that was stored frozen in the presence or absence of air and with and without heat treatment. Differences in color were detected, however no differences in any of the sensory attributes were detected during 12 months of frozen storage, suggesting that the flavor of pawpaw pulp is stable during frozen storage. The comprehensive analysis of the sensory and quality of pawpaw pulp described in this paper, including the development of a defined, standardized pawpaw sensory lexicon, is an important step in the evolution of pawpaw research.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.270
GPT teacher head0.434
Teacher spread0.164 · 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