Errata: Water Main Break Rates in the USA and Canada: A Comprehensive Study
Bibliographic record
Abstract
Page 5 – Major Finding 6 (change also made in text on Page 18): Added “in the reported pipe inventory” to better clarify the percentage reduction Page 6 – Major Finding 14 (change also made in text on Page 31): Changed “six” to “five” years to explain the time elapsed between the 2018 and 2023 studies Page 7 – Major Finding 28 (change also made in text on Page 46): Added “percentage” to better clarify the percentage of acceptance Page 8 – Section 1.1: Updated “(WRF, 2017)” to “(Grigg, 2007)” and “(US Conference of Mayors, 2018)” to “(Anderson, 2018)” Page 25 – Figure 22: Added “in the basic survey” to the note Page 30 – Figure 29: Reversed the bars so that “12-months” appears before “Five Years” Page 44 – Section 7.0: Replaced “construction-related failure rate” with “construction-related failures” since “rate” was incorrect Page 51 – References: Updated “Water Research Foundation, “Asset Management: Breaks & Leaks,” 2017.” to “Grigg, N.S., “Main Break Prediction, Prevention, and Control,” Water Research Foundation, 2007.”
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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 | 0.008 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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".