Bibliographic record
Abstract
Numerous Evolutionary Computations (EC) software tools are now publicly available to the community - see for instance [1] and [2] for a listing of the most well known. The majority of these tools are specific to a particular EC flavor, however, only a few are truly generic EC softwares [3]. The highly diverse and adaptable nature of Evolutionary Algorithms (EA) make generic EC software tools a must-have for rapid prototyping of new approaches. As we all know, EC comprises a broad family of techniques where populations of solutions to problems are represented by some appropriate data structures (e.g. bit strings, real-valued vectors, trees, etc.) on which variation operators (e.g. mutation, crossover, etc.) are applied using iterative algorithms inspired from natural evolution. Different fitness measures can also be used, with one or several objectives, and it is possible to coevolve several species of solutions, with different species represented by possibly different data structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.205 | 0.130 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".