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Record W3150126155 · doi:10.3390/su13073987

Mapping the Vulnerability of Groundwater to Wastewater Spills for Source Water Protection in a Shale Gas Region

2021· article· en· W3150126155 on OpenAlexaff
Teresa Y. Rosales-Ramirez, Dirk Kirste, D. M. Allen, C. A. Mendoza

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of AlbertaSimon Fraser University
Fundersnot available
KeywordsVadose zoneAquiferHydrogeologyWater tableEnvironmental scienceWastewaterGroundwaterHydrology (agriculture)PollutionMODFLOWWater qualityGeologyEnvironmental engineeringWater resource managementGroundwater flowGeotechnical engineering

Abstract

fetched live from OpenAlex

Source water protection in areas of shale gas development encompasses identifying areas that are the most vulnerable to groundwater quality deterioration due to spills of natural gas production wastewater. This study uses the density-dependent flow and transport code TOUGH2 to quantify the time and distance of travel of saline wastewater plumes for different hydrogeological settings in Northeast British Columbia. The models were designed to address three main factors identified from the DRASTIC method for vulnerability assessment: (1) depth to water, (2) impact of vadose zone, and (3) conductivity of the aquifer materials. The vadose zone permeability and depth to water table are dominant controls on the wastewater migration rate and footprint. Overall, the vulnerability in the region is relatively low, with exceptions near river valleys and areas with shallow water tables. The vulnerability maps can be used as a preliminary risk assessment tool, as they are based on the main factors influencing the potential of a wastewater spill to contaminate an aquifer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.542
Threshold uncertainty score0.921

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2021
Admission routes1
Has abstractyes

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