A METHODOLOGY FOR ESTIMATING WATERSHED FUTURE TOTAL IMPERVIOUSNESS THROUGH URBAN GROWTH FORECASTS
Bibliographic record
Abstract
A METHODOLOGY FOR ESTIMATING WATERSHED FUTURE TOTAL IMPERVIOUSNESS THROUGH URBAN GROWTH FORECASTSA methodology was developed by the Greater Vancouver Sewerage & Drainage District (GVS&DD) to estimate future percent total impervious area in stormwater catchments and watersheds using population density. Percent total anthropogenic impervious area is an indicator of watershed health and a parameter associated with stormwater hydrology. Furthermore, parameters that can be forecast into...Author(s)Robert W.B. HicksMichael J. IrvineMark WellmanStan D. WoodsSourceProceedings of the Water Environment FederationSubjectSession 20 - Tools for Aiding in Watershed Management and ModelingDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2000ISSN1938-6478SICI1938-6478(20000101)2000:6L.1917;1-DOI10.2175/193864700785149684Volume / Issue2000 / 6Content sourceWatershed ConferenceFirst / last page(s)1917 - 1924Copyright2000Word count79
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".