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Record W4403138337 · doi:10.5539/jfr.v14n1p34

Relationship Between Aflatoxins Occurrence in Brazil Nuts and the Good Management Practices

2024· article· en· W4403138337 on OpenAlexvenueno aff
Maria Luana Vinhote, Henrique dos S. Pereira, Ériton Gonçalo Rubem, Ariane Mendonça Kluczkovski, Janaína Barroncas, Heloisa Lira Barros, Eirie G. Vinhote, David F. S. Guimaraes

Bibliographic record

VenueJournal of Food Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRadiation Effects and Dosimetry
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado do Amazonas
KeywordsAflatoxinGeographyBusinessBiotechnologyBiology

Abstract

fetched live from OpenAlex

Combining sustainable development with the conservation of the Amazon Forest is a challenge, and one of the strategies could be the strengthening of production chains, such as Brazil nuts. In this context, good practices can be applied to promote the sanitary quality of the seed, in which the presence of a carcinogenic contaminant, aflatoxin, is a continuous issue as it can cause economic embargoes on the product. So, the study aimed to analyse the relationship between several variables for 3 harvests that affected the production of aflatoxin. The results were statistically significant for all extrinsic variables and indicated that treatments with and without good management practices, and interaction between factors influenced the greater occurrence of aflatoxin. Moisture content, as an intrinsic variable to the food, indicated a significant association with Brazil nut contamination

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.136
GPT teacher head0.412
Teacher spread0.276 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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