Bibliographic record
Abstract
Most common diseases and/or their consequences, including hypertension and susceptibility to end-organ damage, have genetic components. Contrary to hereditary Mendelian diseases (where the existence of a rare mutation within a single gene usually dictates whether signs of the disease are present), the manifestations of common diseases correspond to what is defined in genetics as “quantitative complex traits”. Such traits show within populations continuous variation from low to high values, and are shaped by the interactions of a great number of genes (each typically having small effects on their own) with environmental factors. Consequently, identification of genetic variants contributing to complex traits must rely on methods that are different than those used for the identification of Mendelian genetic mutations (which can be performed by following within pedigrees the hereditary transmission of markers linked to the mutated gene). Among several available tools, genetic animal models have proved particularly useful to identify the effects of naturally occurring genetic variants and their effects within mammalian organisms. Indeed, inbred strains are organisms that carry identical copies of each gene. Accordingly, it is possible by performing crosses between them to reduce the complexity of the problem, since the progeny of crosses will carry only 2 possible variants of any gene and experimental and environmental conditions can be controlled.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".