Benign Urban Water Systems: Optimal Complexity, Asynchronous Learning, and User Performance Improvement
Bibliographic record
Abstract
Web instructional use of a group decision support system (GDSS) for design of benign urban water systems is described. A working hypothesis is examined, which proposes that a GDSS can be developed that facilitates demonstrably improved user performance. The idea is based on the observation that experience shows that achievable modeling accuracy depends more on user quality than it does on model structure. PCSWMM was expressly developed for improving user performance and was used in this study. Using this framework, nineteen graduate students from eleven countries participated during Jan–Apr 2000 in a dual graduate course delivered on one web page. Five professors from four Universities collaborated on this project. The courses covered both (1) urban water pollution control planning and (2) urban storm water management. Several modules in the courses focused on attributes supposed to improve model user performance. The study confirms the hypothesis but conclusions are more general: asynchronous internet learning environments may be designed to remove the distinctions between instruction, learning and design (work) and are a useful test bed for the evolution of engineering design technologies. An important concern is that, although it was not measured in this study, model user performance needs to be improved and evaluated objectively. Certain pedagogic innovations were implemented and are briefly reviewed.
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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".