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Record W2006650574 · doi:10.1081/ese-120016890

Laboratory Case Identification of Arsenic in Ronpibul Village, Thailand (2000–2002)

2003· article· en· W2006650574 on OpenAlexaboutno aff
Sumol Pavittranon, Kwanyuen Sripaoraya, Staporn Ramchuen, Sirinmas Kachamatch, Wilaiwan Puttaprug, Narong Pamornpusirikul, Siriluck Thaicharuen, Sutee Rujiwanitchkul, Winai Walueng

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsArsenicIdentification (biology)Environmental scienceGeographyMetallurgyBiologyMaterials scienceEcology

Abstract

fetched live from OpenAlex

Ronpibul village dwellers in the southern part of Thailand have been exposed to arsenic in the water and the environment over three generations. Over the past decades, clean water supplies, utilization and consumption have been introduced to the area. The villagers still use and select rainwater to other forms of potable water. In 2000, the epidemiological survey by Siripitayakunkit (Siripitayakunkit, U. Survey of Chronic Arsenic Poisoning in Ronpiboon, Nakhon Si Thammarat, Thailand, 2000. Proc. 6th International Conference on the Biochemistry of Trace Elements, Guelph, Canada) showed prevalence rate at 24.7%, by using the skin lesion as selection criteria. In 2000-2002, attempt to initiate the local arsenic patient center, we investigated the population at risk in three villages. The laboratory analyses cover urine arsenic level, urine sugar screening and skin lesion classified by dermatologist. The result showed the prevalence of 5.99% of melanosis and 8.67% of hyperkeratosis, 3.84% of urine sugar > 100 mg/dL and 6.33% urine arsenic > 50 microg/g creatinine. There were low to negligible correlation between arsenic urine with urine sugar (r2 = 0.241) and arsenic urine with skin lesion (r2 = 0.058).

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)0.005
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 score0.194
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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

Citations12
Published2003
Admission routes1
Has abstractyes

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