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Physicochemical analysis of apple and grape pomaces

2019· article· en· W2916323988 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsPomaceThermogravimetric analysisSugarThermal stabilityPulp and paper industryFood scienceWineMaterials scienceChemistry

Abstract

fetched live from OpenAlex

This study details a comprehensive analysis of apple and grape pomaces that were generated in the course of juice and wine production, respectively. An extensive physicochemical analysis of these pomaces was performed to determine the elemental composition, ash content, sugar profile, and lignocellulose content. Scanning electron microscopy (SEM) images were taken to examine the morphology of the pomaces. Thermal stability was also examined using thermogravimetric analysis (TGA). Infrared spectroscopy was performed to observe the functional groups on the surfaces of the pomace samples. Grape pomace (GP) had better thermal stability than apple pomace (AP), but washing AP improved its thermal stability. The results from this study provide crucial information for various value-added applications of both apple and grape pomaces, especially for applications which are temperature-dependent. The diversion of these materials from waste back into the economic stream can alleviate their environmental burden and promote sustainable product development.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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