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Record W1555271530 · doi:10.1111/gwat.12091

Anthropogenic Impacts on a Bedrock Aquifer at the Village Scale

2013· article· en· W1555271530 on OpenAlexaff
John C. Kozuskanich, Kent Novakowski, Bruce C. Anderson, Allan S. Crowe, Vimal K. Balakrishnan

Bibliographic record

VenueGround Water · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsEnvironment and Climate Change CanadaQueen's University
Fundersnot available
KeywordsBedrockAquiferGroundwaterOverburdenHydrogeologyEnvironmental scienceHydrology (agriculture)Sampling (signal processing)Indicator bacteriaContaminationFecal coliformGeologyWater qualityWater resource managementMining engineeringEcologyGeomorphology

Abstract

fetched live from OpenAlex

This study focuses on assessing groundwater potability in a highly complex and heterogeneous fractured bedrock aquifer having variable overburden cover. Eight monitoring wells were installed in a privately serviced lakeside village, and groundwater was routinely sampled over a 2-year timeframe for concentration analysis of nitrate, fecal indicator bacteria, stable isotopes, and a total of 41 pharmaceutical compounds. While pollutant concentrations remained low throughout the study, the presence of fecal indicator bacteria and pharmaceuticals was noted at least once (but not always consistently) in most sampling intervals. An interpretation based on the integration of chemical, bacterial, and site characterization datasets suggests that: (1) the fracture network is complex and heterogeneous with limited vertical connectivity; (2) existing pathways are sufficient for the quick and widespread migration of surface contaminants to depth; (3) anthropogenic contaminants from both septic systems and agriculture are likely sourced in the surrounding uplands where overburden is thin; and (4) fecal contamination, as observed over the long term, is ubiquitous at the village scale. Groundwater quality is continually changing in this hydrogeologic environment and the determination of potability on the larger scale is not likely to be adequately captured with infrequent domestic well sampling (i.e., voluntary annual sampling by homeowners).

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 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 score0.052
Threshold uncertainty score0.978

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0400.023

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 head0.008
GPT teacher head0.193
Teacher spread0.184 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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