Bibliographic record
Abstract
So far we have considered characters determined by a single gene with two alleles, occurring in sharply contrasting states, which can have a major affect on the fitness of the organism. In some cases we are justified in modelling selection in this manner, but in many cases, probably the majority, we are not. It is possible to expand the basic theory to consider characters determined by two gene loci, but this approach is no longer useful when we consider characters that are determined by many genes. In these cases we may observe a general relationship between parent and offspring, which suggests that there is an underlying genetic basis to the trait, but we usually do not know how many genes are involved or how they interact. In addition, we may also be aware that the environment influences the trait to some extent. Consequently, in order to study these traits we examine their variability, and attempt to dissect this variation into its genetic and environmental components. This type of analysis is called quantitative genetics. We can consider three types of quantitative traits (Hartl and Clark 1989): Meristic traits in which the phenotype is expressed in discrete, integral classes. Examples include litter size or number of seeds produced per individual, number of flower parts, and kernel colour in wheat. Continuous traits in which there is a continuum of possible phenotypes. Examples include height, weight, oil content, milk yield, human skin colour, and growth rate. In practice, similar phenotypes are often grouped together into classes for the purposes of analysis. […]
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".