Bibliographic record
Abstract
Michael O'Connor, Retired Professional Civil Engineer (Maryland and California), M.ASCE, is a member of the ASCE Committee on Developing Leaders, History and Heritage, Civil Engineering Body of Knowledge (CEBoK), and Engineering Grades.Michael has been a practicing Civil Engineer with over 50 years of engineering, construction, and project management experience split equally between the public and private sectors.Programs ranged from the San Francisco Bay Area Rapid Transit district's 1990's expansions in the East Bay and SFO Airport at three billion to the New Starts program for the Federal Transit Administration with over a hundred projects and $85 billion in construction value.At the latter, he also acted as source selection board chairman and program COTR for $200 plus million in task order contracts for engineering services.Working for the third-largest transit agency in the United States, the Los Angeles County MTA, Michael managed bus vehicle engineering for $1 billion in new acquisitions and post-delivery maintenance support for 2300 vehicles with some of the most complex technology (natural gas engines and embedded systems) in the US transit industry in the 1990s.Michael also has extensive experience as an instructor at New York University (five years), Howard University (four years), and
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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 | 0.638 | 0.685 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".