Exploiting emergent technologies to create systems that meet shifting expectations
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
Properly combined, today’s emerging technolo-gies can potentially lead to systems that adapt quickly and smoothly to ongoing shifts in user requirements and expectations through improving sensing and analytics and utilizing advanced software innovations and service orientation to support dynamic reconfigurations. To produce a system flexible enough to continually meet evolv-ing expectations, various emergent technologies need to be assembled in a coherent fashion based on the capabilities and flexibilities they afford. We outline a framework in which the many activi-ties and choices involved from design to execu-tion and usage of a system can be re-positioned in relation to each other in order to achieve different kinds of flexibility and adaptiveness, taking ad-vantage of data available from sensing mecha-nisms. An example from the transportation domain is used as an illustration. 1
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 | 0.003 |
| 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 | 0.001 | 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