CARDINAL WTRL: TECHNOLOGY MATURITY, SCHEDULE SLIPPAGE AND TREND FORECASTING.
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 Maturity assessments of technology is a crucial process to identify and acquire compatible technologies for a system’s development. However, being a complex and highly subjective process, the Government Accountability Office (GAO) has reported cost overruns and schedule slippages through the years. This study provides a unique Weighted Technology Readiness Level (WTRL) framework which utilizes cardinal factors to ascertain the maturity, schedule and trend of NASA’s 7 Technologies based on their maturity time. The framework utilizes MCDM methods to determine the cardinal complexity of each TRL. It allows the assimilation of other cardinal factors using a simple, open structure to track the overall technology maturity and readiness. Furthermore, this study highlights the importance of tailored TRL frameworks to determine the accurate cardinal coefficient of the said technology and the inferences derived otherwise. It eliminates several limitations of previous frameworks and compares against their performance using a verified statistical representation of processed data.
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