Bibliographic record
Abstract
While it is widely hoped that the human genome project 1 will assist us in treating genetic disease, 2 the race to map the human genome also has a financial incentive. Although the Human Genome Project was completed with public (US) money, some large genomemapping groups have done their work with private funding and have actually patented the DNA fragments that they have mapped. These DNA fragments, called expressed sequence tags (EST), may be thousands of bases in length and may carry real genetic information. EST strands may code for a specific function, so if a scientist finds a useful application in a particular coded sequence and that application depends on the DNA sequence, the application will in effect be patented. Although private firms believe that patenting is necessary to protect their work, it may also restrict innovation, as independent scientists will have no incentive to work on patented sequences. However, the ethical issues related to this topic — even the thought that there could be private ownership of a part of the human body — are incredible. Many groups have pointed out that since genomic information cannot be invented, it should not be considered patentable. Furthermore, since the first person to observe the hypothalamus was not granted title to it or any of its hormones, why should a scientist be given title to a base-pair sequence from any chromosome, just because he or she was the first to sequence that strand? An example of the effect of patenting a sequence or gene on health care relates to breast cancer. In 1994, the BRCA1 gene was identified as part of chromosome 17 and was sequenced. 3,4 Subsequent studies have demonstrated that women with this mutation have a greater risk of breast cancer than those without it. 5
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.104 | 0.057 |
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".