Keynote Speaker: Mary Prettyman
Bibliographic record
Abstract
Mary Prettyman leads the marketing team for Pratt & Whitney’s commercial engines division. In her prior role, she managed sales regions, including the West Asia and Oceania with large fleets in India, New Zealand and Australia, and the Americas sales, closing key customer business with Air Canada and Delta Air Lines among others. Prior to joining Pratt & Whitney in 2015, she enjoyed a 20 year career with Airbus in various leadership roles in sales and marketing for commercial aircraft. She served as Vice President, Marketing - North America from 2008-2013, and prior to that as Sales Director and Marketing Director. In these roles, she closed transactions for hundreds of Airbus aircraft sold to airline customers in the US and Canada.\nPrior to joining Airbus, Ms. Prettyman spent several years in marketing at regional aircraft manufacturer Fokker Aircraft USA. She began her career in Long Beach, CA as an Aerodynamics Engineer for commercial aircraft at Douglas Aircraft Company.\nMs. Prettyman holds a Bachelor of Science degree in Aeronautics and Astronautics from MIT and a Master of Science degree in Civil Engineering – Transportation Studies from the University of California at Berkeley.\nMs. Prettyman serves on the Board of Directors as Secretary and Vice President-elect for ISTAT, the International Society of Transport Aircraft Trading.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".