What the bovine genome project means to New Zealand
Bibliographic record
Abstract
The sequencing of the bovine genome commenced in December 2003 and sequencing is planned to achieve a seven-fold coverage by November 2005. This along with previous physical mapping efforts will allow a high quality genomic assembly to be produced. The sequencing is being undertaken by Baylor College of Medicine in Texas. Separately, the Michael Smith Genome Science Centre in Vancouver will sequence 10,000 full length non-redundant bovine mRNAs. The assembled genome will be annotated in collaboration with the Ensembl project at the Sanger Institute, UK, and will be publicly available on genome browsers such as NCBI, Ensembl and UCSC. In the interim the raw sequence information is being made available at NCBI within days of being generated. As well as providing DNA sequence, the program will identify more than 50,000 microsatellites and one to two million SNPs. The availability of this information will transform and accelerate ruminant genomics research. Similar genome sequencing efforts in human and laboratory species have greatly increased the rate of gene variant discovery underlying quantitative trait loci. It will also aid our understanding of gene expression control. New Zealand stands to benefit directly from this work, because of its strong reliance on grazing livestock for export income and its internationally competitive beef and dairy industries. The project is funded by an international consortium involving CSIRO Australia, Genome Canada, New Zealand, National Human Genome Research Institute of the U.S. national Institutes of Health (NHGRI), U.S. Department of Agriculture (USDA), the State of Texas, The Kleberg Foundation, U.S. Beef Council, and Texas and South Dakota Beef Councils. Significantly, the National Institutes of Health have strongly supported the project as a consequence of its human health implications. This project benefited greatly from a previous international genomic collaboration to physically map the bovine genome, and it provides an excellent example of benefits obtained from international research consortia co-investing in public domain infrastructural projects.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".