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Record W1980270644 · doi:10.3920/wmj2012.1393

Considerations in the preparation of laboratory samples for the analysis of ochratoxin A in wheat

2012· article· en· W1980270644 on OpenAlexaff
Sheryl A. Tittlemier, Mike Roscoe, Colleen Kobialka, Richard Blagden

Bibliographic record

VenueWorld Mycotoxin Journal · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
Fundersnot available
KeywordsOchratoxin AOchratoxinComminutionEnvironmental scienceAnalyserRelative standard deviationWheat grainHomogeneousParticle sizeMoistureMycotoxinMaterials scienceChemistryMathematicsAgronomyFood scienceChromatographyDetection limitMetallurgyBiology

Abstract

fetched live from OpenAlex

A process used to prepare the test portion of ground wheat from the whole grain laboratory sample for ochratoxin A (OTA) analysis using dry comminution with homogenisation and sub-sampling via a rotary sample divider was developed and evaluated. With respect to OTA content, the developed process produced a homogeneous sample of ground wheat from 10 kg of whole grain. Relative standard deviations of the mean OTA concentration for five naturally contaminated wheat samples processed using the developed method ranged from 9% to 19% over a relevant concentration range of 1.7 to 7.6 mg/kg. Additional studies demonstrated that OTA was stable in ground wheat with moisture content between 12 to 13% for at least a year when stored at ambient temperatures. Further examination of the developed comminution and dividing procedure demonstrated that higher concentrations were measured in smaller sized particles, indicating that the accuracy and precision of OTA analyses could be affected by the particle size of ground wheat.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations22
Published2012
Admission routes1
Has abstractyes

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