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
Differential Evolution (DE) has shown a superior performance for solving global continuous optimization problems. The crucial idea of DE is modifying the population of the candidate solutions toward the weighted differences of randomly selected candidate solutions. In this paper, we propose the length scale-based DE which utilizes the obtained information of a landscape analysis metric, the length scale metric, to enhance its own performance. Landscape analysis methods attempt to gain the properties of optimization problems. For two sample points, length scale metric calculates their objective function changes with respect to the distance between them. By computing length scale values of all possible pairs of candidate solutions, DE can employ the pairs of candidate solutions with the greater length scale values to calculate the difference vector in its mutation operator. The length scale-based DE is evaluated on CEC-2014 benchmark functions. Two dimensions, 50 and 100, are considered for benchmark functions. Simulation results confirm that the proposed algorithm obtains a promising performance on the majority of the benchmark functions on both dimensions.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it