{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01748721,0.0005453512,0.0008394139,0.0454577,0.001339231,0.004665654,0.001139335,0.001662585,0.002387961],"category_scores_gemma":[0.1068157,0.0002776201,0.001562978,0.04321128,0.001053006,0.008116985,0.00190844,0.0005257828,0.0005553322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002604211,"about_ca_system_score_gemma":0.001846728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003904512,"about_ca_topic_score_gemma":0.005303411,"domain_scores_codex":[0.986555,0.005129973,0.002231958,0.0007476113,0.004826776,0.0005086732],"domain_scores_gemma":[0.7202662,0.239138,0.01433794,0.004141781,0.02109263,0.001023403],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001317598,0.0007675841,0.1850137,0.01522451,0.001400804,0.003151215,0.03378104,0.004006603,0.0033983,0.01790823,0.0153536,0.7186769],"study_design_scores_gemma":[0.0003168754,0.003023419,0.7072825,0.009336373,0.004017404,0.01318302,0.08258505,0.03260383,0.01089643,0.01507757,0.1212047,0.0004729796],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9137412,0.04161675,0.008016756,0.00143279,0.0001679995,0.0006539336,0.003655517,0.0004367354,0.03027839],"genre_scores_gemma":[0.9572899,0.0182659,0.01828425,0.0002353432,0.0001432198,0.0003475834,0.003711529,0.0001220855,0.001600169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9825128,"threshold_uncertainty_score":0.09248233,"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."}}