Rainfall and microbial contamination in Alberta well water
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".