Bibliographic record
Abstract
stem cell entrepreneurship-trends and advances-II Stem cell research market in the world has grown exponentially over the last decade and in India the total investment is estimated to be about $540 million in 2010 with an annual growth rate of 15%.1 From just a few Institutes in India two years back, today over 30 institutions are involved in stem cell research with the Indian government investing around $8 million dollars in just the last two years. What is the ultimate goal of Stem Cell re-search? Today, the ultimate aim of scien-tists is to be able to build tissues or organs that can replace injured or diseased tissues in the human body. This concept which gives rise to the generation of mature tis-sues has made adult stem cells the focus of intense research, designed to treat a variety of human diseases. In the clinical scenario, stem cells are expected to be transplanted into the damaged area and then grow to a new, healthy tissue. Considering the im-mense interest worldwide, it comes as no surprise that the global market for stem cell therapy is around $20 billion in 2010, as per a Frost & Sullivan study. There are almost 180 prominent companies work-ing on stem cell research in the world, majority of which are based in the US, fol-
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".