Bibliographic record
Abstract
In 1973, scientists assembled at the first Human Gene Mapping Workshop to discuss the 64 human genes mapped at that time. In 1989, the GDB Human Genome Database was created to store information on 1, 700 mapped human genes. Ten years later, as the human genome project closes in on the release of the complete DNA sequence holding as many as 100,000 human genes, GDB is evolving to continue to meet the needs of the scientific community. Well known as a resource for data which has been stringently reviewed as part of the curation process, GDB prepares to continue to provide a compilation of the human genome including maps, map objects, polymorphisms, and mutations. As more sites across the Internet are established to share biological information, it becomes increasingly burdensome for the scientist to collect data from all sources of a particular domain. In an attempt to reduce this burden, GDB continues to load data from large genome centres and accept submissions from researchers around the world. Moreover, GDB looks to provide a mechanism to link gene-related information to the human reference sequence. In doing this, GDB plans to establish federated linkages with "boutique" databases around the world that could contain enormous amounts of valuable information about specific genes or chromosomes.
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 | 0.028 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.020 |
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".