Bibliographic record
Abstract
Abstract Genetic load is the reduction in the mean fitness of a population relative to a population composed entirely of individuals having optimal genotypes. Load can be caused by recurrent deleterious mutations, genetic drift, recombination affecting epistatically favourable gene combinations, or other genetic processes. Genetic load potentially can cause the mean fitness of a population to be greatly reduced relative to populations without sources of less fit genotypes. Mutation load can be difficult or impossible to measure. Many species have mutation rates low enough that substantial genetic load is not expected, but for others, such as humans, the mutation rate may be great enough that load can be substantial. In extremely small populations, drift load, caused by the fixation by drift of weakly deleterious mutations, can threaten the probability of persistence of the population. Migration from other populations adapted to different local conditions can bring in locally maladapted alleles, resulting in migration load. Key Concepts: Genetic load is the reduction in mean fitness of a population caused by some population genetic process. Mutation load is the reduction in fitness caused by recurrent deleterious mutations. Mutation load may be as great as 95% for the human population. Drift load is the reduction in mean fitness caused by genetic drift. In extreme cases, deleterious alleles can reach a frequency of one in a population because of genetic drift. Genetic load can also be caused by recombination breaking up beneficial combinations of alleles, segregation reducing the frequency of fit heterozygotes, or migration bringing less fit alleles into a local population.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.033 |
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".