FAA Center of Excellence for Alternative Jet Fuels and Environment: Annual Technical Report: For the Period October 1, 2017 – September 30, 2018
Bibliographic record
Abstract
This report covers the period October 1, 2017 through September 30, 2018. The Center was established by the authority of FAA solicitation 13-C-AJFE-Solicitation. During that time the ASCENT team launched a new website, which can be viewed at ascent.aero. The next meeting will be hosted by the Georgia Institute of Technology, April 18-19, 2019 in Atlanta. Over the last year, the ASCENT team has made great strides in research, outreach, and education. The team’s success includes the following: 32 research projects. The projects can be divided into five categories: tools, operations, noise, emissions, and alternative fuels. See the project category descriptions for more detail on each category and a summary of the projects. Funding for these projects comes from the FAA in partnership with Transport Canada. 179 publications, reports, and presentations by the ASCENT team. Each project report includes a list of publications, reports, and presentations published between June 2015 and December 2018. A comprehensive list of the publications, reports, and presentations is available in the publications index on page 726. 116 students participated in aviation research with the ASCENT team. Each project report includes the names and roles of the graduate and undergraduate students in the investigator’s research. Students are selected by the investigators to participate in this research. 72 industry partners involved in ASCENT. ASCENT’s industry partners play an important role in the Center. The 72 members of the ASCENT Advisory Board provide insight into the view of stakeholders, provide advice on the activities and priorities of the Center’s co-directors, and ensure research will have practical application. The committee does not influence FAA policy. Industry partners also play a direct role in some of the research projects, providing resources and expertise to the project investigators.\n
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.134 | 0.144 |
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".