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
Extinction is a natural process that drives biological evolution. In this study the impact of introducing extinction operators into ring optimization was examined. Ring optimizers are spatially structured evolutionary optimizers inspired by the biological phenomenon of a ring species. A small initial population is introduced into a ring-structured space and spreads, using the spatial structure to manage the exploration/exploitation trade-off of the algorithm. Extinction operators eliminate a substantial fraction of the current population, in effect resetting the algorithm to a more exploratory state. Two types of extinction operators are tested and compared. The “deluge operator” removes population members with lower fitness while the “asteroid operator” removes population members in a contiguous block of the ring. Three benchmark functions were used, one a discrete simulation and the other two open-ended continuous real functions. The behavior of the extinction operators are different for each of the benchmark functions. The differences in behavior of the extinction operators are explained in terms of the fitness landscapes of the benchmark functions.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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