MétaCan
Menu
Back to cohort
Record W2730822619 · doi:10.4236/jwarp.2017.98059

The Potential Relationship between the Incidence of Neurodegenerative Disease and Trace Mineral Composition in the Drinking Water of Rural Residents of Ontario

2017· article· en· W2730822619 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Water Resource and Protection · 2017
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEnvironmental healthIncidence (geometry)DiseasePotassiumMedicineToxicologyBiologyChemistryInternal medicine

Abstract

fetched live from OpenAlex

Many chronic degenerative diseases have been linked to high intake of various trace and heavy metals. The presence of these compounds in drinking water may be a significant contributing factor to total dietary intake and deposition, resulting in the propagation of a disease cascade. Dairy farm families residing in rural Ontario completed a survey pertaining to the health status of the individuals living on-farm. Water samples were also collected at each location and analyzed for mineral content. Out of 200 surveys delivered to farms, 134 were returned, which formed a study group comprised of 218 adult and 230 children participants. Taking into consideration several factors, such as genetics, environment and diet, the data were analyzed for correlations between heavy metal and mineral status and the prevalence of neurodegenerative diseases. The findings from this study suggest phosphorus, potassium and magnesium concentrations may play a role in the development of neurodegenerative diseases. Total hardness and pH of water may also have an impact on the development of these diseases.

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)0.001
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 score0.074
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.287
Teacher spread0.255 · 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