An empirical approach to the measurement of interchromosomal distances in the genetic algorithm
Bibliographic record
Abstract
Data visualizations, population diversity measurements, and cluster analyses are all invariably constructed from measures of distance or dissimilarity, and it is recognized that any measure of the distance between points should represent the manner and ease with which an algorithm or process can move from one point towards another. For the genetic algorithm, this traversal is largely accomplished by mutation and recombination, but in spite of this, measures like the Hamming distance and the edit distance are still used to assess the distance between population members. This represents a significant problem, because these measures were not designed with the genetic algorithm in mind and they do not consider how the genetic operators will actually traverse genotypic space. The need for distance measures to be accurate and representative cannot be overstated, but for the complex traversals of the genetic algorithm, it is exceedingly difficult to determine whether one measure is any more representative than another. To address this need, this paper will introduce an empirical approach to distance measurement, and since the resultant values are derived from actual traversals, the distance measured is guaranteed representative, and can be used as a baseline against which other measures can be evaluated.
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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 | 0.020 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".