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Record W2147191079 · doi:10.5539/sar.v1n2p178

Evolution of the Merchantability and the Level of Ochratoxin A of Ivorian Cocoa Beans from Production Areas during the Harvest Season

2012· article· en· W2147191079 on OpenAlexvenueno aff
Adama Coulibaly, Ardjouma Dembélé, Henri Biego, Nahoulé Silue, A. Abba Toure

Bibliographic record

VenueSustainable Agriculture Research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCocoa and Sweet Potato Agronomy
Canadian institutionsnot available
Fundersnot available
KeywordsCOCOA BEANOchratoxin AHectarePostharvestAgricultural scienceHarvest seasonToxicologyBiologyMycotoxinBiotechnologyHorticultureFood scienceAgricultureEcology

Abstract

fetched live from OpenAlex

<p>This study aims to investigate the evolution, throughout the harvest season, of merchantability criteria and Ochratoxin A (OTA) levels in cocoa beans produced in Cote d'Ivoire. To this end, 567 samples of cocoa beans, collected in 6 production areas during the 2007-2008 season, were analyzed. Merchantability and OTA levels were determined respectively according to the Ivorian Coffee and Cocoa stock exchange standards and the European Community regulation related to the analytical methods for the control of mycotoxins levels in foodstuffs (EC 401/2006). Concerning merchantability, a significant difference at 5% risk was revealed between the values of moisture, graining and grades. As regards OTA levels, the concentrations obtained ranged from 0.41 µg/kg to 1.36 µg/kg of cocoa beans with an average concentration of 0.69 µg/kg. These concentrations are all below the maximum value set at 2 µg/kg by the European Commission. Moldy and/or slaty beans are chiefly answerable for the depreciation of cocoa beans marketability. These results served to devise a map summarizing the quality of Ivorian cocoa beans. Needless to say, this map is just a representation of a situation at a given time, and should therefore contribute to take up decisions relevant to the application of good production and postharvest processing practices in the country’s quest for cocoa beans of prime quality.</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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.041
GPT teacher head0.270
Teacher spread0.229 · 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 designObservational
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

Citations5
Published2012
Admission routes1
Has abstractyes

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