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Record W4394735220 · doi:10.1139/facets-2023-0062

Survey of rice (<i>Oryza sativa</i> L.) production ecosystems in northern Ghana confirms low risk of exposure to potential toxic elements from local grain consumption

2024· article· en· W4394735220 on OpenAlexvenueno aff
Eureka Adomako, Kow Aboagye-Ghunney, Prince Owusu

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsOryza sativaConsumption (sociology)Production (economics)EcosystemEnvironmental scienceAgronomyBiologyEcologyEconomics

Abstract

fetched live from OpenAlex

Expanding local rice production to meet consumer demand is a priority action under the Government of Ghana’s Planting for Food and Jobs initiative. While studies on yield-enhancing interventions abound, fewer studies focus on food safety issues (e.g., the potential toxic element status of the production ecosystems). This study was, therefore, conducted to bridge the knowledge gap. Chemical analyses were conducted on water, soil, and rice grain samples from different production ecosystems in the Northern and Upper East regions of Ghana. Statistical analysis of the data showed that soil and rice grain arsenic concentrations were significantly higher ( P &lt; 0.001) in the Upper East region. In the Northern region, mean cadmium concentration in rice grains from the irrigated lowland fields (0.023±0.003 mg/kg) was significantly higher than in grains from the rainfed fields. All recorded concentrations of rice grain arsenic, cadmium, and lead were, however, within permissible limits, indicating a low risk of dietary exposure. The observed differences in concentrations within and between regions suggest that soil texture and other geogenic factors could influence the potential toxic element status of the rice production ecosystems. Regular monitoring is, therefore, recommended to maintain the safety of Ghana’s locally produced rice for human consumption.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.001

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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