{"id":"W4254272619","doi":"10.1002/asi.21313","title":"Contextual cocitation: Augmenting cocitation analysis and its applications","year":2010,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto","funders":"","keywords":"Computer science; Granularity; Work (physics)","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":["metaresearch","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01429464,0.00006447262,0.0002191165,0.01295143,0.0008016021,0.0009663443,0.001206899,0.00005161587,0.000005180376],"category_scores_gemma":[0.01836302,0.00003876292,0.0001669465,0.1320689,0.001696264,0.002291498,0.0002751149,0.000261733,0.000003220061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004593582,"about_ca_system_score_gemma":0.0002929296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004831609,"about_ca_topic_score_gemma":0.00000443759,"domain_scores_codex":[0.9963455,0.00001671494,0.0006230182,0.0001450423,0.002650605,0.0002191539],"domain_scores_gemma":[0.9874632,0.001264126,0.001515366,0.0002318503,0.009407567,0.0001179422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001574894,0.00003558492,0.05647589,0.000008709359,0.0001446207,7.905313e-8,0.001322614,0.00003087203,0.03168492,0.0456772,0.002432613,0.8621712],"study_design_scores_gemma":[0.002329824,0.0009793016,0.4582529,0.00001423949,0.0004419592,0.0001680777,0.08014686,0.1263189,0.01662256,0.05030037,0.2638438,0.0005812076],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952792,0.00006694295,0.03873826,0.007851084,0.0001288643,0.0002343223,0.00001592,0.000008764686,0.0001638688],"genre_scores_gemma":[0.99196,0.00008239158,0.007439633,0.0004438802,0.00002487977,0.0000129786,5.799458e-7,0.000001447048,0.00003418755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.86159,"threshold_uncertainty_score":0.9982359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1540436620985238,"score_gpt":0.5017164090871692,"score_spread":0.3476727469886454,"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."}}