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
Abstract Most applications of genomic selection (GS) have so far been in animals, especially dairy cattle, although theoretical studies have also been conducted for maize. For the last 50 years, commercial breeding programmes for dairy cattle have been based on the progeny test scheme, which results in an average generation interval of approximately 7 years for the sire to dam path. It should be possible to double rates of genetic gain by the application of GS, if generation intervals are reduced to close to the biological minimum. During the last decade, methods were developed for high-throughput genotyping of thousands of single nucleotide polymorphisms per individuals. The number of potential polymorphic markers per species was increased from about 1000 to tens of thousands, and costs per individual marker genotype were reduced from several dollars to less than one cent. The application of marker-assisted selection based on genome-wide association studies requires solutions to new statistical problems. Specifically how should information from pedigree, phenotypic records and genotypes be combined to optimally rank candidates for selection? Various linear and Bayesian methods have been proposed and tested to compute genomic estimated breeding values. Linear models require significantly less computing time, and perform nearly as well as Bayesian methodologies. Methods are generally evaluated by comparing genomic evaluations based only on pedigree and genotype to genetic evaluations based on daughter records of the same bulls. Genomic selection programmes for dairy cattle have been implemented in the USA, Canada, Australia, New Zealand and the Netherlands. With declining genotyping costs it becomes economically viable to genotype more individuals, including candidate bull dams. More emphasis can be placed on low heritability traits, such as fertility and disease traits, and it will be easier to control the increase in inbreeding in commercial animal populations.
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 | 0.000 | 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