Bibliographic record
Abstract
Dr. Moallemi is a research associate at the ERAU NEAR laboratory. He received his Bachelor and Master degrees in Computer Engineering and a Ph.D. in Electrical and Computer Engineering from Carleton University in Ottawa, Canada. He worked as a researcher at Carleton’s Advanced Research and Simulation lab for several years and has received numerous awards for his Ph.D. Thesis on simulation development. Since 2013, he has been working at the NEAR Lab on NextGen Tasks for the FAA as a researcher, primarily in simulation and systems. He is also a Principal Investigator in the Alliance for System Safety of UAS through Research Excellence (ASSURE) program and has a comprehensive understanding of aviation/aeronautical standards. He is actively involved as a system engineer and researcher in 4D Trajectory-Based Operations via Aeronautical Telecommunication Network, investigating a simulation-based demonstration of automation in air traffic navigation. This is a 3-year ongoing project funded by FAA in collaboration with Lockheed Martin Corporation, General Electric, Honeywell, ARINC, and LS Technologies in Florida NextGen Testbed, Daytona Beach, FL. As PI, Dr. Moallemi led a team in a 1-year ongoing project demonstrating through simulation, the current surveillance data standards (Mode-S and ADS-B transponders) to be used in future TCAS and UAS detect and avoid systems, developed by RTCA-228. Additionally, he led the research in a 2 phase (2-year) project funded by FAA to investigate Cybersecurity and information integrity in the air to ground transfer of FIXM, AIXM, and WXXM messages.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".