Synthetic Biology Research and Innovation Profile 2018: Publications and Patents
Bibliographic record
Abstract
A profile of synthetic biology research and innovation is presented using data on publications and patents worldwide and for the UK and selected benchmark countries. The search approach used to identify synthetic biology publications identifies a core set of synthetic biology papers, extracts and refines keywords from these core records, searches for additional papers using those keywords, and supplements with articles published in dedicated synthetic biology journals and curated synthetic biology special collections. For the period from 2000 through to mid-July 2018, 11,369 synthetic biology publication records are identified worldwide. For patents, the search approach uses the same keywords as for publications then identifies further patents using a citation-tree search algorithm. The search covered patents by priority year from 2003 to early August 2018. Following geographical matching, 8,460 synthetic biology basic patent records were identified worldwide. Using this data, analyses of publications are presented which look at the growth of synthetic biology outputs, top countries and leading organizations, international co-authoring, leading subject categories, citations, synthetic biology on the map of science, and funding sponsorship. For patents, the analysis examines growth in patenting, national variations in publications compared with patenting, leading patent assignees, and the positioning of synthetic biology on a visualized map of patents.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.062 | 0.083 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".