Bibliographic record
Abstract
The development of distributed urban drainage models is becoming more important as cities prepare for the challenges associated with climate change such as more intense precipitation events (McCarthy et al. 2010; Allan, 2011; Simõeset et al. 2011; Blumensaat et al. 2012; Leitãoet al. 2012). GIS-based tools were developed to generate input datasets for a 1-D distributed urban drainage model for part of Toronto's combined area, resulting in an efficient model development process compared to those utilizing manual approaches. These automatic GIS-based tools included the delineation of Wet Weather Flow (WWF) subcatchments (stormwater) and Dry Weather Flow (DWF) subcatchments (sanitary). It also included the determination of the intensity of rainfall on a more detailed scale than the coarse coverage provided by the City's rain gauges and the traditional Thiessen polygon interpolation method. Through testing the new tools designed in ModelBuilder, it was determined that 66% and 52% of DWF and WWF subcatchments respectively, were automatically delineated to a degree where they would be "Acceptable" for input into the urban drainage model, InfoWorks CS. Although the rainfall tools were able to continuously interpolate measured rainfall (on a seemingly unprecedented basis),and generate over 700 virtual rain gauges, the validity of the approach remains imperfect due to irresolvable inconsistencies between the City's gauges and those used for validation purposes.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".