{"id":"W1990834318","doi":"10.1002/asi.20617","title":"Can citation analysis of Web publications better detect research fronts?","year":2007,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Web visibility and informetrics","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Florida State University","keywords":"Citation; Period (music); Field (mathematics); Computer science; Web of science; Lagging; Citation analysis; Data science; Bibliometrics; World Wide Web; Political science; MEDLINE; Statistics; Mathematics","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.008144194,0.00005218988,0.0001868293,0.003130865,0.0004064818,0.0001407822,0.001414218,0.00004775214,7.25835e-7],"category_scores_gemma":[0.002078914,0.00003559517,0.0001853303,0.02984892,0.001727607,0.002577357,0.0002370437,0.0002334366,5.00297e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001638246,"about_ca_system_score_gemma":0.000530739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001719908,"about_ca_topic_score_gemma":0.00001482892,"domain_scores_codex":[0.9982095,0.00001440554,0.0005600228,0.00008632267,0.0008805864,0.0002491865],"domain_scores_gemma":[0.9946297,0.0003057049,0.0009923456,0.0003802809,0.003634377,0.00005752442],"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":[0.00002063274,0.00006092861,0.03232065,0.00002615322,0.0003510483,7.607247e-8,0.005115225,0.0001328583,0.009054761,0.08293579,0.005764307,0.8642176],"study_design_scores_gemma":[0.001513769,0.002052061,0.5940201,0.0000420435,0.000375299,0.00006865275,0.03123651,0.1995392,0.03857573,0.03624199,0.09584871,0.0004859229],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7590067,0.00002151614,0.2252181,0.01516638,0.00008357169,0.0001513619,0.000007309419,0.00001896433,0.0003260714],"genre_scores_gemma":[0.9512319,0.00004299812,0.04796774,0.0007365962,0.00001002958,0.000002892931,4.657959e-7,0.000001002232,0.000006397064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8637316,"threshold_uncertainty_score":0.9907721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03885566806836807,"score_gpt":0.3482058520080921,"score_spread":0.309350183939724,"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."}}