An Interactive Database Supporting Virtual Fieldwork in an Environmental Engineering Design Project
Bibliographic record
Abstract
Abstract This work presents an interactive simulation software package for delivering an environmental engineering design project. The primary goal of the effort is to supplement theory‐based course content with a complex and relevant design project, and to do so without increasing student course loads or placing excessive time demands on instructors. An additional goal for research‐based instructors is to provide an efficient mechanism for infusing current research findings and experimental techniques into the curriculum. The software that administers the design project is called Interactive Site Investigation Software (ISIS). This paper summarizes the rationale for the development of ISIS, outlines the instructor‐generated input required by ISIS, and details current ISIS features. These features allow students to drill boreholes, collect core samples, construct wells, collect groundwater samples, submit samples for laboratory analysis, and execute hydraulic and transport experiments at a virtual hazardous waste site. Initial feedback on the usability and usefulness of ISIS was generally positive, and the automated data requisition and dispensation substantially reduced the project's administrative demands on the instructor. Common student complaints pertained to controlled access to the software in the face of deadline pressure, uncertain expectations regarding their work product, and the need for real‐time advice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".