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Record W6994075882

Josephine Burnett

2016· article· en· W6994075882 on OpenAlexaboutno aff

Bibliographic record

VenueScholarly Commons (Embry–Riddle Aeronautical University) · 2016
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPayload (computing)International Space StationSpace ShuttleLaunchedSpace (punctuation)Test (biology)NASA Chief ScientistSpace researchSpace technology
DOInot available

Abstract

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Josephine Burnett is the director of Exploration Research and Technology Programs at NASA's John F. Kennedy Space Center in Florida. She is responsible for strategic leadership and program and project management for Kennedy support to the Exploration mission, which includes Space Life and Physical Sciences (SLPS), International Space Station (ISS), Advanced Exploration Systems (AES), and Space Technology (ST) Programs. Burnett began her career with NASA in 1987 at Kennedy as an aerospace/mechanical engineer integrating experiments onto Spacelab racks and pallets. In 1991, she became an experiment project engineer and led a team of engineers in the test and checkout of the United States Microgravity Laboratory–1 Spacelab Module. Burnett moved to an operations position in 1993 as a payload test director. In this position, she was responsible for overseeing shuttle payload testing for assigned payloads, including payload support for launch countdown. In 1995, Burnett served as the center director management Intern and performed in this capacity until 1996 when she joined the Space Station Hardware Integration Office. For three years, she supported the test and checkout of the Canadian Space Station Remote Manipulator System (SSRMS) in Brampton, Ontario, and the Canadian portion of the Multi Element Integrated Test, after which time she was selected to lead the Element 6A Office as the acting chief. In 2000, Burnett joined the ISS/Payload Processing Directorate as the chief of the Future Missions and International Partner Division. In this position, she led the office responsible for the advanced planning of ISS international elements, shuttle payloads, and concepts for improved ways to test payloads in the future. In 2003, she returned to the directorate as deputy for Program Management for the ISS and Payload Processing Directorate and the Kennedy Space Center. In this capacity she supported the director in the development of organizational roles and responsibilities, as well the development of long-range organizational direction for Kennedy and ISS Program needs. Burnett was selected as a member of the Senior Executive Services Career Development Program (SESCDP) class of 2004 and completed the program in March 2006. She returned to Kennedy as the chief of the Systems Engineering and Integration Division for Kennedy’s Design Engineering Directorate. In this capacity, she led a team of highly experienced systems engineers in support of the Constellation Ground Operations Project. Burnett then became the deputy director of the Space Transportation Planning office, providing strategic guidance and leadership for the program management and systems engineering and integration of the Constellation Block 1 Orion/Ares configuration to low-Earth orbit. Mostly recently, she was the director of the ISS Ground Processing and Research Project Office responsible for all ground processing of space station elements from around the world. These elements are operating in orbit and supporting the largest, most complex space station in human history. Burnett graduated from the University of Florida in 1987 with a B.S. in aerospace engineering and, in 1991, earned a Master of Science from the Florida Institute of Technology in space system operations. She is a native of Florida and lives in Merritt Island with her family.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.155
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.1550.073

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.

Opus teacher head0.012
GPT teacher head0.167
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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