AC 2007-2480: BUILDING SECURITY AND BIO-CHEMICAL TERRORISM? AN INTERDISCIPLINARY COURSE
Bibliographic record
Abstract
also the Project Manager of the US EPA funded project on developing educational materials for building professionals which focuses on protecting buildings against chemical and biological attacks. His research interests have been focused on optimization and economic analysis of mining and energy projects, Monte Carlo simulation, real options analysis, carbon management, and computer modeling of energy technologies. He is a member of both the Society for Mining, Metallurgy and Exploration (SME), the Canadian Institute of Mining, Metallurgy and Petroleum Engineering (CIM) and the Air and Waste Management Association (AWMA). He has experience in the exploration and production of crude oil and natural gas as well as in coal, gold, and other metallic and non-metallic mining. Harmohindar Singh, North Carolina A&T State University Professor of Architectural and Mechanical Engineering and by training is a mechanical engineer. He has 42 years of teaching, research and development experience in heating, ventilating, and air conditioning (HVAC) systems, indoor air quality (IAQ) and energy conservation in built environment. He is director of an interdisciplinary center for energy research and technology (CERT) which has been recognized as a unique center by the University of North Carolina (UNC) system. Dr. Singh has conducted approximately 123 energy assessment studies for small and medium scale industries for US DOE under Energy Analysis and Diagnostic (EADC) program from 1984 to 1989. He has arranged over 100 one-day or half-day workshops for the designers and consulting engineers, facility managers/engineers and other professionals and
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".