Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this review, I describe how evolutionary genomics is uniquely suited to spearhead advances in understanding human disease risk, owing to the privileged position of genes as fundamental causes of phenotypic variation, and the ability of population genetic and phylogenetic methods to robustly infer processes of natural selection, drift, and mutation from genetic variation at the levels of family, population, species, and clade. I first provide an overview of models for the origins and maintenance of genetically based disease risk in humans. I then discuss how analyses of genetic disease risk can be dovetailed with studies of positive and balancing selection, to evaluate the degree to which the 'genes that make us human' also represent the genes that mediate risk of polygenic disease. Finally, I present four basic principles for the nascent field of human evolutionary medical genomics, each of which represents a process that is nonintuitive from a proximate perspective. Joint consideration of these principles compels novel forms of interdisciplinary analyses, most notably studies that (i) analyze tradeoffs at the level of molecular genetics, and (ii) identify genetic variants that are derived in the human lineage or in specific populations, and then compare individuals with derived versus ancestral alleles.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it