Water Infrastructure: Information on Financing, Capital Planning, and Privatization
Bibliographic record
Abstract
A chapter report issued by the General Accounting Office with an abstract that begins "According to the Environmental Protection Agency (EPA) and water utility industry groups, communities will need as much as $1 trillion during the next 20 years to repair, replace, or upgrade aging drinking water and wastewater facilities; accommodate a growing population; and meet new water quality standards. GAO found that the amount of funds obtained from user charges and other local sources of revenue was less than the full cost of providing service--including operation and maintenance, debt service, depreciation, and taxes--for more than a quarter of drinking water utilities and more than 4 out of 10 wastewater utilities in their most recent fiscal year. GAO also found that more than a quarter of utilities lacked plans recommended by utility associations for managing their existing capital assets, but nearly all had plans that identify future capital improvement needs. A privatization agreement's potential to generate profits is the key factor influencing decisions by private companies that enter into such agreements with publicly owned utilities or the governmental entities they serve, according to the companies GAO contacted."
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".