Bibliographic record
Abstract
The Human Genome Project (HGP) was an international scientific research project with a primary goal to determine sequence of chemical base pairs which make up DNA and to identify and map approximately 20,000–25,000 genes of human from both a physical and functional standpoint. The first available assembly of was completed in 2000 by UCSC Genome Bioinformatics Group, composed of Jim Kent (then a UCSC graduate student of molecular, cell and developmental biology), Patrick Gavin, Terrence Furey and David Kulp. The project began in 1990, initially headed by James D. Watson at U.S. National Institutes of Health. A working draft of was released in 2000 and a complete one in 2003, with further analysis still being published. A parallel project was conducted outside of government by Celera Corporation. Most of government-sponsored sequencing was performed in universities and research centers from United States, United Kingdom, Canada, and New Zealand. The mapping of human genes is an important step in development of medicines and other aspects of health care. The HGP originally aimed to map nucleotides contained in a haploid reference human (more than three billion). Several groups have announced efforts to extend this to diploid human genomes including International HapMap Project, Applied Biosystems, Perlegen, Illumina, JCVI, Personal Genome Project, and Roche-454. The of any given individual (except for identical twins and cloned organisms) is unique; mapping the human genome involves sequencing multiple variations of each gene. The project did not study entire DNA found in human cells; some heterochromatic areas (about 8% of total genome) remain un-sequenced.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.189 | 0.119 |
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".