Bibliographic record
Abstract
Ellen Ebner is the Director for Sustainable Technologies supporting the Sustainability & Future Mobility team at Boeing. Ebner and the team are focused on developing technologies that will enable Boeing and our industry’s pathway to net zero carbon emissions by 2050. Previously, Ebner led operations for the Enterprise Technology Strategy team to apply portfolio management, product management and technology road mapping methodologies to technology management. She has also served in Boeing leadership roles supporting Interiors Responsibility Center Fabrication and Assembly, 787 Program Final Assembly and Delivery and BR&T Materials & Manufacturing Technology. Her prior work experience is in renewable energy and energy efficiency design & consulting. Ebner holds master's degrees in Business Administration and Engineering Systems from MIT (Leaders for Global Operations program) and a bachelor’s degree in Bioresource Engineering from McGill University.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".