Modeling of Urban Water Systems: Web and Internet Access to Technical Literature (Refereed and Gray) and to User Experience
Bibliographic record
Abstract
Internet list-servers at the University of Guelph have for some time been used to aid public-domain design software developed or supported by the USEPA (e.g. swmm-users, wasp-users, epanet-users) USGS (hspf-users) and the US Army Corps of Engineers (hec-users), as well as for other special interests (e.g. urban-rain). Subscriptions number in the low thousands. Communications have been characterized by an exceptionally high signal-to-noise ratio, and extensive, useful archives have been developed. Together with search engines, the archives have been made available on the web. In addition a bibliographic indexing system (BIBLIO2002) has been developed based on published gray (e.g. proceedings of conferences) and refereed literature for a selected limited number of keywords. 10000 abstracts have been collected in the database and made available on a CDROM together with a search engine that allows the usual bibliographic SQL searches. Both databases are incorporated in a decision support shell for the Storm Water Management Model. The paper describes the databases, the software tools used to build the archive, gives the sources for accessing them, and provides sample searches.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".