{"id":"W3084591110","doi":"10.3386/w27803","title":"Linguistic Metrics for Patent Disclosure: Evidence from University Versus Corporate Patents","year":2020,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Intellectual Property and Patents","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Business; Linguistics; Accounting; Psychology; Computer science; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.008925043,0.0001475385,0.0002602862,0.006155894,0.0008320427,0.002540549,0.0005188072,0.0007850549,0.001760506],"category_scores_gemma":[0.1149211,0.00008767071,0.000329663,0.00708485,0.001455084,0.003844194,0.001654653,0.001069445,0.0003190521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007255953,"about_ca_system_score_gemma":0.0006230779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001739566,"about_ca_topic_score_gemma":0.001288145,"domain_scores_codex":[0.9924523,0.003173162,0.0008664626,0.0006975738,0.00253391,0.000276456],"domain_scores_gemma":[0.7476922,0.1618523,0.07212359,0.006956039,0.009368724,0.002007214],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004165616,0.0002975147,0.865885,0.0002482829,0.0001747015,0.000267032,0.005030779,0.003138353,0.001647135,0.01151778,0.001510222,0.1098667],"study_design_scores_gemma":[0.00002558857,0.0003970395,0.9605794,0.0001553403,0.0001124175,0.000807672,0.003053744,0.008765053,0.004058135,0.0153171,0.00665653,0.00007199849],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986141,0.001938412,0.002723687,0.001156196,0.0000241722,0.00001580501,0.0004120457,0.00002685943,0.007561866],"genre_scores_gemma":[0.9986664,0.0002694946,0.0005606451,0.00005841223,0.00004532352,0.000007407763,0.0001800895,0.000005689281,0.0002065186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9938441,"threshold_uncertainty_score":0.04720068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8697513441641661,"score_gpt":0.4530571413381441,"score_spread":0.416694202826022,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}