{"id":"W4389126707","doi":"10.59627/cbens.2014.2180","title":"COMPREENSÃO DE MUDANÇAS CLIMÁTICAS REGIONAIS ATRAVÉS DA APLICAÇÃO DE TRÊS MÉTODOS ESTATÍSTICOS","year":2014,"lang":"pt","type":"article","venue":"Anais Congresso Brasileiro de Energia Solar","topic":"Geography and Environmental Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Snow; Cluster (spacecraft); Climate change; Trend analysis; Climatology; Statistical analysis; Geography; Environmental science; Physical geography; Meteorology; Statistics; Mathematics; Geology","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001362877,0.001130417,0.001030069,0.0001903578,0.001461914,0.000334943,0.0014479,0.0007136558,0.002185521],"category_scores_gemma":[0.0003167535,0.00120272,0.0007017886,0.0006468502,0.002305494,0.0005182136,0.0009833012,0.001003433,0.00110877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007602683,"about_ca_system_score_gemma":0.00006709359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005237196,"about_ca_topic_score_gemma":0.001243558,"domain_scores_codex":[0.9926892,0.0009600641,0.0009262383,0.001619604,0.00100307,0.002801803],"domain_scores_gemma":[0.9959415,0.0007709112,0.000470254,0.001430195,0.00003109812,0.001356061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001430001,0.0006863663,0.9451247,0.0001159016,0.0003619043,0.0002235643,0.002379389,0.005520377,0.006799958,0.0007373929,0.01265036,0.02525707],"study_design_scores_gemma":[0.00175687,0.0005554762,0.9401855,0.000199604,0.00051863,0.0001417455,0.001431339,0.0233626,0.004078097,0.0009656235,0.02532888,0.001475628],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752107,0.001736605,0.01550596,0.001378658,0.0005133857,0.0005756484,0.0001653199,0.0002301772,0.00468356],"genre_scores_gemma":[0.9896261,0.002940433,0.002297317,0.003385324,0.0002653521,0.0001337486,0.00006347115,0.0001609955,0.001127278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02378144,"threshold_uncertainty_score":0.9998381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01623775518817274,"score_gpt":0.2471886616610791,"score_spread":0.2309509064729064,"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."}}