Technology readiness levels: Shortcomings and improvement opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The technology readiness level (TRL) scale was developed at the National Aeronautics and Space Administration (NASA) in the 1970s as a standardized technology maturity assessment tool for use in complex system development. Today, TRL assessments are used to make multimillion‐dollar decisions at NASA and beyond, yet anecdotal evidence suggests that there are challenges associated with TRL use in practice. In this paper, we systematically uncover the practitioners' view, first via 19 interviews with employees from seven organizations. We identify 15 challenges of TRL implementations in three categories: system complexity, planning and review, and validity of assessment. Next, we prioritize these challenges via a survey of TRL practitioners, using a best‐worst choice experiment. Finally, we identify best practices and proposed extensions to address the challenges. We find that system complexity challenges are most critical to TRL users, despite being addressed in the literature. We posit that addressing these opportunities could result in substantial improvements to decision processes and outcomes in complex engineering projects.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it