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Record W2989586923 · doi:10.3390/w11122510

Anthropic and Meteorological Controls on the Origin and Quality of Water at a Bank Filtration Site in Canada

2019· article· en· W2989586923 on OpenAlexafffundabout
Janie Masse-Dufresne, Paul Baudron, Florent Barbecot, Marc Patenaude, Coralie Pontoreau, Francis Proteau-Bédard, Matthieu Menou, Philippe Pasquier, Sabine Veuille, Benoît Barbeau

Bibliographic record

VenueWater · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversité du Québec à MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique Montréal
KeywordsMixing (physics)Forcing (mathematics)Environmental scienceWater qualityAnthropic principleFiltration (mathematics)Hydrology (agriculture)GCM transcription factorsClimatologyClimate changeGeneral Circulation ModelGeologyEcologyOceanographyMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

At many bank filtration (BF) sites, mixing ratios between the contributing sources of water are typically regarded as values with no temporal variation, even though hydraulic conditions and pumping regimes can be transient. This study illustrates how anthropic and meteorological forcings influence the origin of the water of a BF system that interacts with two lakes (named A and B). The development of a time-varying binary mixing model based on electrical conductivity (EC) allowed the estimation of mixing ratios over a year. A sensitivity analysis quantified the importance of considering the temporal variability of the end-members for reliable results. The model revealed that the contribution from Lake A may vary from 0% to 100%. At the wells that were operated continuously at >1000 m3/day, the contribution from Lake A stabilized between 54% and 78%. On the other hand, intermittent and occasional pumping regimes caused the mixing ratios to be controlled by indirect anthropic and/or meteorological forcing. The flow conditions have implications for the quality of the bank filtrate, as highlighted via the spatiotemporal variability of total Fe and Mn concentrations. We therefore propose guidelines for rapid decision-making regarding the origin and quality of the pumped drinking water.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score1.000

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)0.0010.000

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 head0.019
GPT teacher head0.224
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 teacher head, not a consensus.

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

Citations14
Published2019
Admission routes3
Has abstractyes

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