{"id":"W7011393537","doi":"","title":"Mapas da especialização trimestral de chuva e hietrograma trimestral da região metropolitana de Teresina / PI","year":2019,"lang":"pt","type":"article","venue":"Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research)","topic":"Geography and Environmental Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Spatial distribution; Precipitation; Metropolitan area; Spatial 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.00301479,0.001036669,0.0009262571,0.004155424,0.001691937,0.003494332,0.0022418,0.0007147927,0.00198955],"category_scores_gemma":[0.001279955,0.001049202,0.001030534,0.01020115,0.00352833,0.003564884,0.001561157,0.001294852,0.002062474],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003896148,"about_ca_system_score_gemma":0.0007608092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001240142,"about_ca_topic_score_gemma":0.0001886018,"domain_scores_codex":[0.9887491,0.0003405708,0.001073616,0.002075339,0.004243725,0.003517609],"domain_scores_gemma":[0.9964008,0.0006595282,0.0004174726,0.001060999,0.0002261525,0.00123502],"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":[0.004682994,0.007635357,0.1168825,0.001001616,0.002100189,0.0003018058,0.003333659,0.008329867,0.01566703,0.2284446,0.5838097,0.02781067],"study_design_scores_gemma":[0.005381386,0.002609353,0.08890877,0.000429328,0.0001658942,0.0001590055,0.005851676,0.001225834,0.003423716,0.01387305,0.8757738,0.002198178],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8827451,0.002766369,0.0018716,0.009501887,0.002972287,0.005429906,0.008171983,0.0003136043,0.08622727],"genre_scores_gemma":[0.9790875,0.0008611589,0.001170676,0.0001474048,0.0007055319,0.0002070461,0.0006697525,0.0001452146,0.01700571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2919641,"threshold_uncertainty_score":0.9999277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07654967782679102,"score_gpt":0.3476996043673632,"score_spread":0.2711499265405722,"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."}}