{"id":"W4230637358","doi":"10.21741/9781945291838-20","title":"Bibliometric Analysis of Scientific and Technical Papers within NOCMAT 1984-2015 International Conferences","year":2018,"lang":"en","type":"article","venue":"Materials research proceedings","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Library science; Bibliometrics; Computer science; Regional science; Data science; Political science; Engineering ethics; Sociology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004922107,0.0003923723,0.001000095,0.1178886,0.001183649,0.00475571,0.0009436731,0.0005618189,0.004953724],"category_scores_gemma":[0.03221129,0.0001631332,0.001009053,0.1474376,0.0006715127,0.002334058,0.002296761,0.0004193967,0.001303287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003090081,"about_ca_system_score_gemma":0.003471443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00882005,"about_ca_topic_score_gemma":0.01162045,"domain_scores_codex":[0.9889654,0.001104782,0.001839526,0.0007859208,0.006695561,0.0006088452],"domain_scores_gemma":[0.9629838,0.01147372,0.008968272,0.001150058,0.01395921,0.001464852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007889855,0.0002152288,0.6194978,0.007866497,0.001767015,0.0006426558,0.003558219,0.00200793,0.004232762,0.007072319,0.06299725,0.2893533],"study_design_scores_gemma":[0.00002205974,0.00007880214,0.95251,0.0006720871,0.0004113389,0.0003650218,0.002845381,0.001266405,0.00160254,0.0009666473,0.03920156,0.00005814208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8337513,0.04099843,0.002031337,0.002913075,0.0006214179,0.0003401221,0.06054695,0.0003817145,0.05841572],"genre_scores_gemma":[0.9419431,0.01594957,0.002207442,0.0001569115,0.000746102,0.0002950805,0.0309183,0.00006693749,0.007716435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8821114,"threshold_uncertainty_score":0.0260309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06681742497530792,"score_gpt":0.4054121410326889,"score_spread":0.338594716057381,"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."}}