IMAGE: A Computer-Aided Cognition Capability for Understanding Complex Systems
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
Motivation – Computational models are increasingly used to gain insights into complex phenomena. However, better tools are needed to analyze and understand them. Research approach – We describe the IMAGE technology and an ongoing empirical evaluation of IMAGE compared to a baseline condition. Findings/Design – IMAGE provides interactive visualizations to facilitate the management and comparison of multiple simulations, the exploration of data, and the explicit representation of knowledge. Research limitations/Implications – While it may not be possible to fully understand nor predict the behaviour of complex systems, analysts and decision makers would benefit from technologies designed to facilitate their analysis. Originality/Value – IMAGE is a set of advanced technologies working in synergy that seeks to augment comprehension of complex systems. Take away message – IMAGE provides powerful innovative tools for simulation, exploration, and representation—three functions that the human mind cannot perform effectively in complex settings without the support of cognitive artifacts
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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