{"id":"W6976682194","doi":"10.60692/6hcsx-epa47","title":"Dossiê - 3º Simpósio Brasileiro de Comunicação Científica – SBCC, 2012.","year":2012,"lang":"pt","type":"article","venue":"Greater South Information System","topic":"Scientific Research and Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Public policy; Statistical analysis","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003477099,0.00137518,0.000690863,0.005037284,0.002487328,0.005560841,0.001188226,0.002130107,0.06396449],"category_scores_gemma":[0.01043037,0.0007695243,0.0004421464,0.008593773,0.001416084,0.00365668,0.002690651,0.002297904,0.01918917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008070416,"about_ca_system_score_gemma":0.02030407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1952863,"about_ca_topic_score_gemma":0.2139383,"domain_scores_codex":[0.9956988,0.000623724,0.0003984504,0.0005841215,0.00214897,0.0005459168],"domain_scores_gemma":[0.9907892,0.00109322,0.000536864,0.0008387498,0.005032302,0.00170979],"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":[0.0001262488,0.00005482156,0.004635105,0.0009561232,0.00001891387,0.0001935441,0.001302137,0.0003257306,0.001361973,0.02927844,0.6766875,0.2850596],"study_design_scores_gemma":[0.000004219935,0.00001272622,0.006538589,0.0004821396,0.000006808575,0.00009351837,0.0005060736,0.00009358305,0.0003514293,0.0009249216,0.9909723,0.00001379288],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01145271,0.1530504,0.008809444,0.05384413,0.01086705,0.0006910008,0.04170751,0.002504211,0.7170736],"genre_scores_gemma":[0.1516539,0.0792722,0.01556001,0.005182138,0.001032704,0.0007073931,0.02710152,0.001849684,0.7176405],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9944392,"threshold_uncertainty_score":0.3882992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07040882751930594,"score_gpt":0.2693035361323332,"score_spread":0.1988947086130273,"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."}}