Bibliographic record
Abstract
Here we demonstrate the models formation and research findings of the Replicator Dynamics that interpret the evolutionary process, also the discussion about problem with the models which are commonly used in evolutionary game theory, i.e. the difference between the fitness of population following a certain strategy and the average fitness of the entire population determine the change of population proportion following the strategy between generations, that is to say what happen after one bout are used to interpret the phenomena before the bout. As a modification, we construct a model directly with the numbers of the population following a certain strategy between generations, and prove the characteristics of its key can be discussed according to the model. Key words: adaptation, evolutionary dynamics, Replicator Dynamics Resume: Ici, nous demontrons la formation des modeles et la conclusion de la recherche de la dynamique de la reproduction qui interprete le proces evolutionniste, et aussi la discussion sur le probleme avec les modeles qui sont communement utilises dans la theorie du jeu evolutionniste, c.-a-d. la difference entre l’aptitude de la population suivant une certaine strategie et l’aptitude en moyenne de la toute la population determinent le changement de la proportion de la population suivant la strategie parmi les generations, c’est-a-dire ce qui se passe apres un acces sont utilise pour interpreter les phenomenes avant un acces. Comme une modification, nous construisons un modele directement avec les nombres de la population selon une certaine strategie parmi les generations, et prouvent que les caracteristiques de leur cle peuvent etre discute d’apres le modele. Mots-Cles: adaptation, dynamique evolutionniste, dynamique de reproduction
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".