Bibliographic record
Abstract
Michael Peck is an Adjunct Assistant Professor at Embry-Riddle Aeronautical University - Worldwide where he teaches courses in Aviation/Aerospace Law, Business Law and Aviation Legislation. In addition, he is a retired Partner in the Global Finance Group of Sidley Austin LLP, a global law firm of more than 1800 lawyers with offices in the United States, Europe and Asia. Primarily located in the New York City office, Mr. Peck practiced for 36 years in the area of asset-backed finance (including aircraft finance) and structured securitization. He is admitted to practice in the federal and state courts of New York and Georgia as well as before the Supreme Court of the United States. He is currently the Chair of the Aviation Finance Subcommittee of the Association of the Bar of The City of New York. After a four-year period of active duty with the United States Army in Asia and Europe, Mr. Peck remained in the Army Reserves, rose to the rank of Lieutenant Colonel and retired in 1998. Mr. Peck is a graduate of the Institute of Air and Space Law at McGill University (Montreal, Canada), has JD and MBA degrees from Vanderbilt University (Nashville, Tennessee), an MA degree in Anthropology from Duke University (Durham, North Carolina) and a BA degree from Washington & Lee University (Lexington, Virginia). He is also a graduate of (i) the Defense Strategy Course at the U.S. Army War College, (ii) the U.S. Army Command and General Staff College and (iii) the Department of Defense Equal Opportunity Management Institute. An avid pilot since 1978, Mr. Peck holds a commercial pilot’s certificate with instrument rating as well as a remote pilot’s certificate with small unmanned aircraft systems rating. He is also a certified flight instructor, instrument instructor and advanced ground instructor.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.262 | 0.151 |
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".