Strategies for Patenting in Biotechnology
Bibliographic record
Abstract
Intellectual property rights (IP) is one of the major component of research in many organizations (both profit and non-profit), institutes and academics. Since 1970’s, during which a Canadian non-governmental organization (ETC group) filed two patent applications for the first time on “the world's firstever human-made life form”, many companies including academic institutes or universities are encouraging their researchers to protect their findings through IP's. It is obvious for the researcher to surprise if he looks at the number of patents that were issued since 1970 on various entities over the advancement of science. Despite intense database on inventions and/or discoveries of various scientific organizations, the increasing interests of the scientists to protect their inventions/technology/discovery thorough IP is significantly reducing the accessibility of their findings and there by slowing advances in science. In this review, we are discussing on various components of patenting tools, protection and methodologies as an introductory material for scientists and students for the better understanding of intellectual property (IP) rights. We would like to promote the use of IP's to protect the technology being theft out for biological terrorism rather than a commercial motif to “Business” the science.
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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".