MétaCan
Menu
Back to cohort
Record W2515777002 · doi:10.1680/jenes.15.00022

Rainfall and microbial contamination in Alberta well water

2016· article· en· W2515777002 on OpenAlexaffvenueabout
Caterina Valeo, Sylvia Checkley, Jianxun He, Norman F. Neumann

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsProvincial Laboratory of Public HealthUniversity of AlbertaUniversity of CalgaryUniversity of Victoria
Fundersnot available
KeywordsPrecipitationEnvironmental scienceContaminationSpatial variabilitySpatial analysisFecal coliformHydrology (agriculture)Veterinary medicineEcologyBiologyGeographyWater qualityMathematicsRemote sensingGeologyMeteorologyStatistics

Abstract

fetched live from OpenAlex

Spatial and seasonal patterns in the positive rates of total coliforms and Escherichia coli in Alberta well water were investigated to gain insight into well water microbial contamination. Analysis was conducted in the presence of total coliforms (77 135 tests) and E. coli (77 132 tests) in well water from 2004 to 2009 along with monthly estimates of precipitation, all of which were aggregated to 13 zones across Alberta by using Voronoi tessellation. Regression combined with autocorrelation analysis was employed to develop wave functions for data assembled in each zone. Precipitation was found to peak in June or July in all 13 regions. The positive E. coli rate was found to peak in June, July or August, but the positive total coliform rate peaked in August, September or October. Spatial statistical analysis revealed a potential association of total coliforms and E. coli with precipitation in two heavily populated basins. Spatial density analysis revealed a cluster of positive tests of total coliforms and E. coli in a narrow spatial extent in June and July of 2005.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.866
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Environmental Engineering and ScienceSame topicFecal contamination and water qualityFrench-language works237,207