Bibliographic record
Abstract
Traditionally, a genetic algorithm is used to analyze net-works by maximizing the modularity (Q) measure to create a favorable community. A coevolutionary algorithm is used here to not only find the appropriate community division for a network, but to find interesting networks containing sub-stantial changes in data within a very large network space. The network is one of the largest, if not the largest, analyzed by evolutionary computation techniques to date and is cre-ated using a real world data set consisting of fisheries catch data in the north Atlantic Ocean off the coast of Canada. This work examines the quantitative performance of two types of coevolutionary algorithms against both a standard GA that uses a natural (but not necessarily optimal) division of the data set into communities, and simulated annealing. The goal for all search algorithms was to automatically find anomalies (differences in catch) within the data. To measure practical usefulness of the system, a fisheries expert analyzed the best networks located by the search algorithms using an existing visualization software prototype. The expert indi-cated that a refined version of coevolutionary GA known as PAMDGA was found to most reliably locate subnetworks containing catch differences of biological relevance. Categories and Subject Descriptors
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.578 | 0.228 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".