Discussion of “Statistical Evaluation of Factors Affecting Indicator Bacteria in Urban Storm-Water Runoff” by J. M. Hathaway, W. F. Hunt, and O. D. Simmons III
Bibliographic record
Abstract
For a residential watershed in Raleigh, North Carolina, the authors monitored storm-water runoff for indicator bacteria during 20 rain events, and statistically evaluated factors affecting such bacteria. The original paper and a follow-up paper (Hathaway and Hunt 2011) increased understanding of indicator bacteria export from urban watersheds and identified factors contributing to the complexity of such export. The authors may want to comment on how their Raleigh study relates to a recent study byHe et al. (2010), who evaluated indicator bacteria during 10 rain events in storm-water runoff from a residential watershed in Calgary, Canada. The following additional comments and questions are intended to obtain additional information about the Raleigh experimental watershed runoff statistics for each studied Raleigh rain event.
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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 | 0.117 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".