{"id":"W2591927979","doi":"10.1002/bul2.2017.1720430311","title":"SIG/MET: METRICS 2016: Workshop on Informetric and Scientometric Research","year":2017,"lang":"en","type":"article","venue":"Bulletin of the Association for Information Science and Technology","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Informetrics; Scientometrics; Bibliometrics; Event (particle physics); Presentation (obstetrics); Library science; Computer science; Data science; 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":[0.08829896,0.003198665,0.003485943,0.02166088,0.0033253,0.02708823,0.00477577,0.007099349,0.03869983],"category_scores_gemma":[0.09690755,0.001528829,0.002549348,0.0125041,0.004597666,0.01745403,0.01109403,0.008595784,0.03613191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00938462,"about_ca_system_score_gemma":0.02008775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004350603,"about_ca_topic_score_gemma":0.008000303,"domain_scores_codex":[0.9547826,0.01438516,0.004204914,0.003450808,0.02083208,0.002344479],"domain_scores_gemma":[0.8541534,0.0271151,0.007820373,0.009297269,0.06932891,0.03228489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004322133,0.00003012792,0.000213456,0.0002217769,0.00001711841,0.0000448157,0.000111715,0.0001360082,0.0001329571,0.003575332,0.9524128,0.04306068],"study_design_scores_gemma":[0.00001452874,0.00002143383,0.0005365054,0.0003267009,0.000008000993,0.0000694433,0.0001777341,0.0003278926,0.0001440524,0.00600664,0.9923428,0.00002443284],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.002418756,0.06143126,0.03113547,0.5023333,0.3450596,0.0007108784,0.003783575,0.003893918,0.04923323],"genre_scores_gemma":[0.04009184,0.08607047,0.05668114,0.05527001,0.4382653,0.00223299,0.01123749,0.009455935,0.3006949],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9783391,"threshold_uncertainty_score":0.4669752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1003186983138181,"score_gpt":0.3594559513531396,"score_spread":0.2591372530393214,"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."}}