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Record W4387439716 · doi:10.1080/19440049.2023.2264976

Diverging fates of cadmium and glyphosate during pasta cooking

2023· article· en· W4387439716 on OpenAlexaff
Sheryl A. Tittlemier, Lianna Bestvater, Jason Chan, Valentina Timofeiev, Anja Richter, Kun Wang, Yuefeng Ruan, Marta S. Izydorczyk, Bin Xiao Fu

Bibliographic record

VenueFood Additives & Contaminants Part A · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCadmiumGlyphosateChemistryBranFood scienceAgronomyBiology

Abstract

fetched live from OpenAlex

Durum wheat cultivars with varying abilities to accumulate cadmium were grown and treated in the field with a glyphosate-containing herbicide at different stages of maturity to produce grain with higher and lower concentrations of cadmium (0.066-0.214 mg/kg) and glyphosate (0.474-0.874 mg/kg). The grain was milled, and fractions were analysed for cadmium and glyphosate. The highest concentrations for both cadmium and glyphosate were associated with bran and shorts, although the percentage of total cadmium mass in bran (23-25%) was less than glyphosate (38%). The preparation of dried pasta from semolina and flour milling fractions reduced concentrations by a factor of 1.8 for glyphosate and 1.4 for cadmium. Dried pasta was cooked and analysed along with the cooking water for cadmium and glyphosate at seven-time points from 0 to 15 min. Concentrations of glyphosate in cooked pasta decreased significantly with cooking time; no decrease was observed for cadmium concentrations. Analysis of cooking water demonstrated that glyphosate migrated from pasta to the cooking water. After 15 min of cooking, approximately 73% of the total glyphosate mass had transferred from pasta to cooking water. Over the same time period, only 5% of the total cadmium mass had transferred from pasta to cooking water.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.999

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 science0.0000.001
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 head0.015
GPT teacher head0.234
Teacher spread0.218 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFood Additives & Contaminants Part ASame topicHeavy metals in environmentFrench-language works237,207