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Record W1617843090 · doi:10.5539/jfr.v4n5p25

Physicochemical Properties of Melipona beecheii Honey of the Yucatan Peninsula

2015· article· en· W1617843090 on OpenAlexvenueno aff
Víctor M. Moo‐Huchin, Gustavo A. González‐Aguilar, Jose D. Lira-Maas, Emilio Pérez‐Pacheco, Raciel Javier Estrada‐León, Mariela I. Moo-Huchin, Enrique Sauri‐Duch

Bibliographic record

VenueJournal of Food Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHydroxymethylfurfuralStingless beeSugarFood scienceHoney beeMoistureBiologyEnvironmental scienceBotanyChemistryApidaeHymenoptera

Abstract

fetched live from OpenAlex

<p>The knowledge regarding the physicochemical characteristics of the honey produced by stingless bees is still limited, mainly due to the high diversity of the floral resources and the low production that is inherent to these species. This manuscript describes the physicochemical characterization of 27 honey samples produced by <em>Melipona beecheii, </em>from the Yucatan Peninsula, Mexico. The objective of this study was to contribute to the establishment of standards for quality control. The following parameters were evaluated in the honey samples: reducing sugars, moisture content, acidity, pH, hydroxymethylfurfural (HMF), ash, soluble solids, formol index, proline and color. Most of physico-chemical parameters fulfilled the quality criteria established by the International Legislation for Apis honey, with the exception of moisture content, which presented higher values; for that, the results indicate that the international standard procedures are not completing adequate for all the parameters analyzed on <em>Apis mellifera</em> honey and therefore is need establish a suitable standard of quality control for honey from <em>Melipona</em>.<em> </em></p>

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 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)0.002
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.198
GPT teacher head0.311
Teacher spread0.113 · 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 teacher head, 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

Citations25
Published2015
Admission routes1
Has abstractyes

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