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Record W2183147524 · doi:10.2166/wqrj.2001.030

Farm Well Water Quality in Alberta

2001· article· en· W2183147524 on OpenAlexaffabout
Darcy A. Fitzgerald, D. S. Chanasyk, R. David Neilson, Dave Kiely, Robert J. Audette

Bibliographic record

VenueWater Quality Research Journal · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital EdmontonUniversity of AlbertaAgriculture Food and Rural Development
FundersMinistry of EnvironmentU.S. Environmental Protection Agency
KeywordsWater qualityAnimal scienceChemistryEnvironmental chemistryAquiferGroundwaterEnvironmental scienceBiologyGeologyEcology

Abstract

fetched live from OpenAlex

Abstract On-farm groundwater supplies in Alberta were evaluated for chemical (routine chemistry, trace metals), herbicides and microbiological (total and fecal coliforms) parameters to determine the suitability of domestic drinking water usage based on the Guidelines for Canadian Drinking Water Quality (GCDWQ). The sampling program was conducted between May and October of 1995 and 1996. Thirty-two percent of the 816 farm water wells surveyed (depth range 2 to 284 m) exceeded the GCDWQ for maximum acceptable concentration (MAC) or interim maximum acceptable concentration (IMAC) of at least one parameter. In addition, the water from 92% of the sites exceeded the GCDWQ for at least one of the aesthetic objectives (AO). The chemicals were ranked from most to least frequently exceeding the GCDWQ MAC, in the following order: F >> NO3 −N + NO2−N > As > Se > Pb > B > U > Cr (13, 6, 3, 3, 2, 0.9, 0.4 and 0.2% of all samples, respectively). The parameters ranked from most to least frequently, exceeding the AO, in the following order: TDS > Na > Fe > Mn > pH > SO4 > Cl > Al > Zn > Cu (85, 64, 36, 34, 23, 19, 6, 2, 1 and 0.1 % of the samples, respectively). The majority of the higher concentrations of most inorganic parameters are due to natural geological conditions defined by source aquifer mineralogy. The effects of primary agriculture are likely limited to the 3% herbicide detections and to some nitrate and microbiological contaminations observed; this water should not be used for human consumption without some form of site-specific treatment. Some rural residents may be “mistreating” their water, and a general lack of water testing among rural residents was noted.

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)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.004

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 head0.105
GPT teacher head0.368
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2001
Admission routes2
Has abstractyes

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