333 Animal –GRIN a platform for animal genetic information
Bibliographic record
Abstract
Abstract An information system, Animal-GRIN, has been constructed as part of the U.S., Brazilian, and Canadian livestock genetic resource programs. It is designed to provide information to gene bank managers, the research community, and livestock producers about livestock breeds and subpopulations acquired in gene bank collections. The system was developed using a range of free software tools, including: MySQL, Ruby on Rails, Java Script, etc. The system is dynamic and publically accessible (https://nrrc.ars.usda.gov/A-GRIN). Exemplary information in Animal-GRIN consists of: animal identifiers, number and type of samples in the collection, pedigrees, coefficients of genetic relationships between animals within a breed, breeding values, phenotypes, and geographic source. To meet the national need for the long term archiving of genomic information developed with public funds, Animal-GRIN was expanded to store and make publically available genomic information (SNP) from any SNP chip, including custom products. Researchers are encouraged to submit their data upon completion of their publically funded projects. With the drill down concept, users can search the database for genomic information, physical samples associated with the genomic information, and phenotypic information on specific animals. Once animals of interest are found, on-line tools enable users to request either germplasm samples or genomic data. Progress in meeting genetic security for a breed’s collection can also be viewed. To date the U.S. collection has 52,639 animals with almost a million samples representing 36 species, 167 breeds, and 331 subpopulations and these have been entered into Animal-GRIN. Genomic data has been acquired on 1,899 animals representing 36 breeds. The next phase of Animal-GRIN development will be development of landscape genomics components. Acquisition of germplasm samples and associated genomic information are a continuing effort.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.128 | 0.164 |
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".