Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p><span style="font-weight: bold;">This study has been suspended due to a change in platform architecture. We apologise for any inconvenience this may cause. We hope to reinstate this study as soon as possible.</span></p><p><br></p><p>The Organisation for Economic Co-operation and Development (OECD) Patent Statistics are presented in the following tables:<br> <br> Indicators of international co-operation.<br> <br> This dataset presents statistics on Indicators of international co-operation in patents (EPO, USPTO and PCT): where EPO stands for European Patent Office, USPTO for US Patent and Trademark Office and PCT for Patent Cooperation Treaty. Those indicators analyze to cross-border ownership of patents reflecting international flows of knowledge from the inventor country to the applicant countries and international flows of funds for research (multinational companies) and co-inventions representing the international collaboration in the inventive process. Data are divided in terms of Patent office and Triadic Patent families (application filed under EPO, patent grants at the USPTO, patent application filed under the PCT), type of international Cooperation in Patenting (foreign ownership, domestic ownership, percentage of patents invented abroad), reference date (application date, priority date, date of grant) and partner country. Data are presented as annual datapoints from 1976 onwards. The countries covered are Australia, Canada, Japan, Netherlands, United States and the European Union.<br> <br> Patents by main technology and by International Patent Classification (IPC).<br> <br> This dataset comprises statistics on patents by main technology and International Patent Classification (IPC). EPO, USPTO, PCT and Triadic Patent Families are in fact presented according to classes of the International Patent Classification (IPC class up to 4 characters) and for selected technology domains such as ICT, nanotechnology, biotechnology as well as environment-related technologies. Data are presented from 1976 onwards. The countries covered are Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States, Algeria, Andorra, Argentina, Armenia, Belarus, Bermuda, Bosnia and Herzegovina, Brazil, Bulgaria, Cayman Islands, China, Colombia, Costa Rica, Croatia, Cuba, Cyprus, Djibouti, Ecuador, Egypt, El Salvador, Georgia, Guatemala, Hong Kong Special Administrative Region of China, India, Indonesia, Iran (Islamic Republic of), Jamaica, Jordan, Kazakhstan, Kenya, Korea (Democratic People's Republic of), Kuwait, Latvia, Lebanon, Liechtenstein, Lithuania, Macedonia, Malaysia, Malta, Moldova (Republic of), Monaco, Mongolia, Morocco, Nigeria, Pakistan, Panama, Peru, Philippines, Puerto Rico, Romania, Russian Federation, Saudi Arabia, Seychelles, Singapore, South Africa, Sri Lanka, Chinese Taipei, Thailand, Trinidad and Tobago, Tunisia, Ukraine, United Arab Emirates, Uruguay, Uzbekistan, Venezuela, Zimbabwe, Former Yugoslavia.<br> <br> Patents by regions.<br> <br> This dataset includes statistics on patent counts by regions where EPO and PCT filings are presented according to the region of the inventors/applicants residence (Territorial Level 3), including total patents and selected technology domains such as ICT, nanotechnology, biotechnology as well as environment-related technologies. Reference regions are available by inventor’s country of residence and applicant’s country of residents. Data are presented from 1978 onwards. The data covers some regions in Japan, Finland and Belgium.<br> <br> These data were first provided by the UK Data Service in March 2015.<br> </p>
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.226 |
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 it