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Record W3209424021 · doi:10.1128/spectrum.00179-21

Surface Water Intrusion, Land Use Impacts, and Bacterial Community Composition in Shallow Groundwater Wells Supplying Potable Water in Sparsely Populated Areas of a Boreal Region

2021· article· en· W3209424021 on OpenAlexaboutno aff
Kevin J. Lyons, Anna-Maria Hokajärvi, Jenni Ikonen, Ari Kauppinen, Ilkka T. Miettinen, Tarja Pitkänen, Pekka M. Rossi, Katharina Kujala

Bibliographic record

VenueMicrobiology Spectrum · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersMaa- ja Vesitekniikan Tuki RyMaa- ja MetsätalousministeriÖAcademy of FinlandKAUTE-Säätiö
KeywordsGroundwaterEnvironmental scienceWater qualitySurface waterAquiferMicrobial population biologyTurbidityIndicator bacteriaWater wellFecal coliformHydrology (agriculture)Environmental chemistryEcologyBiologyEnvironmental engineeringBacteriaGeologyChemistry

Abstract

fetched live from OpenAlex

Standard physicochemical water quality analyses and microbial indicator analyses leave much of the (largely uncultured) complexity of groundwater microbial communities unexplored. This study combined these standard methods with additional analyses of stable water isotopes, bacterial community data, and environmental data about the surrounding areas to investigate the associations between physicochemical and microbial properties of 28 shallow groundwater wells in Finland. We detected impaired groundwater quality in some wells, identified potential land use impacts, and revealed indications of surface water intrusion which were negatively correlated with bacterial alpha diversity. The potential influence of surface water intrusion on groundwater wells and their bacterial communities is of particular interest and warrants further investigation because surface water intrusion has previously been linked to groundwater contamination, which is the primary cause of waterborne outbreaks in the Nordic region and one of the major causes in the United States and Canada.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.999

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.210
Teacher spread0.189 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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