MétaCan
Menu
Back to cohort
Record W2884487143 · doi:10.1080/19393210.2018.1502818

Aflatoxin B <sub>1</sub> and zearalenone in soybeans: occurrence and distribution in whole and defective kernels

2018· article· en· W2884487143 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

VenueFood Additives and Contaminants Part B · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsMemorial University of Newfoundland
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsAflatoxinMycotoxinZearalenoneGrading (engineering)Incidence (geometry)ContaminationMathematicsFood scienceBiology

Abstract

fetched live from OpenAlex

Few studies have addressed the distribution of mycotoxins in soybean and/or their processing fractions. In this study, samples from commercial lots were collected in four Brazilian states. The distribution of mycotoxins in soybean fractions, according to their commercial grading system, namely whole kernels (WK), split, broken and crushed kernels (SBCK), damaged kernels (DK), heat damaged and burned kernels (HDBK), moldy kernels (MK), greenish kernels (GK), foreign material + impurities (FMI), were analyzed using HPLC-FLD. AFB1 and ZEN tested positive in 43.3 and 80%, respectively. The incidence of AFB1 was higher in MK (50%), followed by HDBK (30.4%) and FMI (26.0%). ZEA incidence ranged from 69% (SBCK) to 100% (HDBK). Co-occurrence (53.3%) in at least one fraction was also detected. Brazil is the second world producer of soybeans, which places the country in a very important position. Therefore, the information provided is crucial and timely relevant for the industry and policymakers.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.325

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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