Improving groundwater treatment decisions using <i>Giardia</i> and total aerobic spores to assess surface water influence
Bibliographic record
Abstract
ABSTRACT Giardia and Cryptosporidium detection in public water supply (PWS) ground water is rare. PWSs are identified as ground water under the direct influence of surface water (GWUDI) using microscopic particulate analysis (MPA) to determine GWUDI. A Canadian dataset of 1,221 samples from 590 ground water devices was collected during the years 2006–2020. Samples were analyzed using the suggested MPA method (for diatoms), for total aerobic spore (TAS), and for parasitic protozoa (EPA Method 1623) (727 samples using the EPA MPA-suggested method, 494 samples using US EPA Method 1623). Giardia cysts were found in 21 samples collected from 16 drinking water production devices. Cryptosporidium oocysts were found in three devices, co-occurring with Giardia. These detections in routine PWS samples using US EPA Method 1623 are the most robust reported detections worldwide. A generalized linear model was used to determine the co-occurrence of TAS or diatom with Giardia and demonstrated that diatoms supplemented by TAS were better than diatoms alone. Diatoms and TAS have complementary parameter sensitivity and specificity when analyzed by sample and by device, i.e. sensitivity (by sample): TAS 78%; diatoms 24% and specificity (by device): TAS 39%; diatoms 87%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".