{"id":"W4205128105","doi":"10.21680/2447-0198.2017v1n3id11003","title":"Seminário do CCSA-UFRN: mapeamento temático das produções científicas na área de Ciência da Informação","year":2017,"lang":"pt","type":"article","venue":"Revista Informação na Sociedade Contemporânea","topic":"Information Science and Libraries","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Humanities; Geography; Art","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":[0.00595688,0.0006696676,0.0007792514,0.009369113,0.002098963,0.007834614,0.0013083,0.001139952,0.02281946],"category_scores_gemma":[0.02257685,0.000498467,0.001095467,0.01725628,0.001798721,0.005208456,0.003083795,0.001352618,0.003301561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006249605,"about_ca_system_score_gemma":0.007639914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05392405,"about_ca_topic_score_gemma":0.05171538,"domain_scores_codex":[0.9967344,0.0009661098,0.0001845808,0.0004316353,0.00154235,0.0001410126],"domain_scores_gemma":[0.9869478,0.006650144,0.0007117892,0.001423616,0.003649479,0.0006172024],"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":[0.0002236501,0.0001262926,0.02984142,0.002269737,0.0001214203,0.0005950311,0.02949227,0.01149911,0.01037774,0.1217903,0.123894,0.669769],"study_design_scores_gemma":[0.00001873453,0.00008893834,0.05167767,0.001014706,0.00009169856,0.0004795341,0.01568577,0.026915,0.007118797,0.04086384,0.8559189,0.0001262911],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1874532,0.01947372,0.3677024,0.04886258,0.002932977,0.001074688,0.03176545,0.009832763,0.3309022],"genre_scores_gemma":[0.5975032,0.0160774,0.2787798,0.001276869,0.0006655441,0.0009880121,0.01309017,0.003507989,0.08811096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9906309,"threshold_uncertainty_score":0.1072204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05186569232995909,"score_gpt":0.3237649702042633,"score_spread":0.2718992778743042,"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."}}