Bibliographic record
Abstract
The State of the Industry (SOTI) survey, now in its fourth year, has compiled a wealth of trending data on the water industry. These data, reflecting input from utility representatives, service providers, and other professionals across the United States and Canada, help illuminate the water industry's current and future concerns. The 2007 survey results indicated that the industry workforce and the sustainability of its human resources was a prime concern for many respondents. Although the workforce had already established a profile as a long‐term concern, this year the category rose to the fifth spot on the list of near‐term critical issues. By highlighting current and emerging concerns as well as inadequately addressed issues, the SOTI survey helps ensure that water professionals have the tools and strategies they need in order to meet the challenges ahead. The top five critical issues identified by the survey include: regulatory factors including concerns about the scientific basis for new regulations and the value of new regulations relative to their cost; source water supply and protection centering on ensuring adequate quantities of treatable water supplies for growing needs and protecting water sources; business factors such as the expense and financing of infrastructure replacement and the imbalance between the cost of delivering quality water service and the rates that can be charged; aging water supply infrastructure, the prospect of its failure, cross‐connection concerns, water leakage and accounting, and water storage; and, workforce issues such as replacement of aging workers, difficulty in recruiting qualified new or replacement workers, and overall training of the workforce to meet increasing sophistication in water operations.
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 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 | 0.001 | 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".