Bibliographic record
Abstract
The results are in from the second annual State of the Industry survey – a comprehensive evaluation of the water industry's overall health. The 2005 State of the Industry report presents key findings from the survey, which included the responses from more than 1,700 utility personnel, service providers, and other individuals. Like the 2004 report, this year's report helps recognize and track significant trends, identify critical and emerging issues – especially those being inadequately addressed – assess water professionals' perception of the relative health of the industry, and provide data that can help the industry prioritize programs and services. In 2005, survey respondents considered the most critical issues facing the overall water industry to be regulatory factors, business factors, source water supply, security, and water storage/ infrastructure, and they identified industry leadership as a new issue. In addition, the 2005 report includes a new look at issues broken down by key US regions and from a Canadian perspective. This year's report also identifies barriers to preparedness and provides a look at utility capital spending; it offers insight into areas of strength and weakness in today's water industry and potential future challenges. The information contained in this article can be used to give direction to the industry's current and future leaders and guide them in directing resources for the healthiest tomorrow.
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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.087 |
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".