The genetic revolution in artificial reproduction: a view of the future
Bibliographic record
Abstract
With the completion of the human genome project, micro-array technology offers the potential to open up a whole new vista in assisted reproduction. In the next 10-20 years we will be able to screen each human embryo for all numerical chromosomal abnormalities as well as many genetic diseases. Micro-array analysis may permit the screening of multiple alleles for monogenetic diseases and polygenic diseases, including diabetes, hypertension and schizophrenia. In the near future, it may be possible to assess an individual's genetic predisposition for cardiovascular disease, all types of cancer and infectious diseases. In the distant future, it may even be possible to screen for any genetic trait, e.g. stature, baldness, obesity, hair colour, skin colour or even IQ. Although it is still uncertain what molecular genetic tools may be available, we can be sure that some of these trends will have major consequences on the future of assisted reproduction and society at large.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".