Bibliographic record
Abstract
Since 2004, AWWA has conducted the State of the Industry survey to take the pulse of the water industry, compiling detailed and comprehensive data about critical issues and concerns. This year, more than 2,000 utility representatives, service providers, and other professionals in the United States and Canada contributed to the 2006 survey. Respondents rated the top five critical issues facing the industry as infrastructure, regulatory factors, business factors, source water supply and protection, and workforce. This was the first time that workforce issues, i.e., retaining and replenishing water industry personnel, edged into the top five, with many respondents citing it as an inadequately addressed area. Meanwhile, security concerns have declined steadily over the three years of the survey. AWWA gathers this information annually to help its members and the water community identify overarching concerns, overlooked issues, and the soundness of the industry. With these data, the industry can revise and fine‐tune its agenda to help ensure its continued growth and success.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.063 | 0.016 |
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".