Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
<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>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,226 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle