Bibliographic record
Abstract
DNA is the code for what we call GENES, genes are the holder of all our personality traits like our hair colour, eye colour, how tall or short we are, etc.We have of thousands of genes.David Suzuki has talked a lot about genes, and how some genes carry illnesses (Suzuki, 2019), like different forms of cancer, cystic fibrosis, sickle cell anemia and many more illnesses that doctors struggle to find cures for.Now scientists think they have found a way to eliminate these illnesses forever.They have developed a strategy called CRISPR; it is a scientific method to place genes back into cells.This basically means they can extract one bad gene (a gene that carries an illness or disease), and then modify it and put it back.Like if you were to need a new battery for your phone, first they open it up, find the problem, remove the problem, replace with a new "battery" and close the phone back up and it's ready to go.Problem solved, easy as that.CRISPR is the remove, fix and replace method scientists have discovered to help cure that "bad" gene that makes faulty proteins that is killing hundreds of thousands of people every year.Not only have scientists discovered that they can remove, fix and replace parts of genes in living humans, they have also discovered that they can use CRISPR to potentially create what we call "designer babies".Designer Babies are basically totally customized humans.You could pick everything from hair and eye colour to personality traits like being artistic or athletic.The real question is should scientists be doing these things, just because they could with a little more technology and understanding how genes work.Is there really a need for totally customized babies.CRISPR can be used to edit the genes in body cells, that can ultimately be used for curing illnesses and diseases.Or it can be used to edit genes in germ line cells, to prevent diseases.Germ cell editing can be inherited, so if they are changed a gene, then the edited gene will be passed along to the next
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.016 |
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".