Technology readiness levels: Shortcomings and improvement opportunities
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.
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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 | 0.050 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".