{"id":"W1982037740","doi":"10.1118/1.4735301","title":"SU‐E‐T‐237: Leading 25 in 25: A Bibliometric Analysis of Classics Articles in IMRT","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Bibliometrics; Citation; Citation analysis; Library science; Scientometrics; Web of science; Subject (documents); MEDLINE; Medicine; Computer science; Meta-analysis; Political science; Internal medicine","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004414784,0.000651851,0.00125721,0.06463014,0.001242669,0.004586093,0.0007793891,0.0004440573,0.008786307],"category_scores_gemma":[0.02925484,0.000199811,0.002526751,0.09692293,0.0006529868,0.002328127,0.00164368,0.0004117496,0.001990394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002142461,"about_ca_system_score_gemma":0.003716262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005108746,"about_ca_topic_score_gemma":0.005506346,"domain_scores_codex":[0.9957937,0.0007263594,0.0009567283,0.000359135,0.001882605,0.0002814641],"domain_scores_gemma":[0.9812726,0.00844183,0.00407314,0.0006862449,0.004679842,0.0008463499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001044733,0.0002258565,0.7548211,0.003994896,0.001937483,0.001102834,0.003308603,0.001258058,0.001678057,0.003363176,0.02689661,0.2003686],"study_design_scores_gemma":[0.0001190875,0.0004863642,0.9378239,0.0008759909,0.001985892,0.001452071,0.005099411,0.004937476,0.002280342,0.002627881,0.04219879,0.0001126922],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9394905,0.006964482,0.003246473,0.001064787,0.0003172382,0.0008377045,0.02757264,0.0004508506,0.02005522],"genre_scores_gemma":[0.9720488,0.003126823,0.005145787,0.0001098214,0.0003435955,0.0005240552,0.01482674,0.00009985251,0.003774558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9353698,"threshold_uncertainty_score":0.0293932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5356024017038159,"score_gpt":0.569783587338663,"score_spread":0.03418118563484707,"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."}}