Bibliographic record
Abstract
Ana Rivera is a senior program integration manager in the Program Business Office of NASA’s Launch Services Program (LSP) at the John F. Kennedy Space Center in Florida. As Program Integration Manager, she serves as a key member of the mission integration team. She is responsible for participating in the initiation, planning, and execution of all matters associated with launch service contracts and associated mission budgets. Ana began her career with NASA in 2005 with the Shuttle Processing Directorate as an electrical engineer. In this role, she served as a Remote Manipulator System (RMS) engineer and was responsible for processing, integration, and testing of the Space Shuttle RMS, also known as the Canada arm, and the inspection boom assembly. During her tenure with the Shuttle Program, Ana also served on a Source Evaluation Board in support of the Constellation Program. In 2010, she began serving as a business integration engineer in the LSP Business Office and moved into her current role in 2013. Ana was born and raised on Florida’s Space Coast. She graduated with a Bachelor’s of Science degree in Electrical Engineering and a Master’s of Science degree in Industrial Engineering from the University of Florida in Gainesville. Ana currently resides in Merritt Island, FL with her husband and their young daughter.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.179 | 0.078 |
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".