{"id":"W2555224843","doi":"10.18260/p.24551","title":"Patent “Sightings”: A Comparative Analysis of Patent Citation Search Tools Using Case Studies from the Engineering Literature","year":2015,"lang":"en","type":"article","venue":"","topic":"Research, Science, and Academia","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Citation; Scopus; Computer science; Search engine indexing; Promotion (chess); Information retrieval; Citation analysis; Value (mathematics); Order (exchange); Patent visualisation; Data science; World Wide Web; Political science; Business; MEDLINE","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005655388,0.0001358378,0.0004530117,0.0005551459,0.000206339,0.0005827755,0.0006199182,0.0000531344,0.00004584056],"category_scores_gemma":[0.003480525,0.00006441826,0.0001651108,0.00560959,0.0001838196,0.000700032,0.0002578265,0.0002742113,0.00001555105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008169915,"about_ca_system_score_gemma":0.0000929384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005243705,"about_ca_topic_score_gemma":0.0002115801,"domain_scores_codex":[0.99601,0.000409927,0.0006097114,0.0004277028,0.002255532,0.000287098],"domain_scores_gemma":[0.993589,0.003903951,0.0001683515,0.0003981722,0.001770973,0.000169553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001235822,0.0001100488,0.06914537,0.00001353722,0.001733958,0.0004882113,0.2921618,0.6076279,0.01352727,0.002767497,0.00441671,0.007884175],"study_design_scores_gemma":[0.0002844486,0.00005969154,0.01385048,0.00005025051,0.0001392509,0.00001960297,0.09029072,0.8912633,0.002489543,0.001113905,0.0002818599,0.0001569132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925973,0.001227796,0.005176988,0.0003684908,0.0001174414,0.0001885701,0.00005624973,0.00001431497,0.0002527981],"genre_scores_gemma":[0.9975725,0.00003087473,0.002061465,0.00006495607,0.00005803298,0.000006001766,0.000007866516,0.000002910939,0.0001953605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2836355,"threshold_uncertainty_score":0.5619718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7492227252729702,"score_gpt":0.5097307820572476,"score_spread":0.2394919432157225,"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."}}