{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006066943,0.0009036186,0.001435146,0.004516963,0.0005227909,0.002763462,0.0008214246,0.0004980374,0.001782101],"category_scores_gemma":[0.01302231,0.0004180107,0.001313266,0.005050331,0.0003039087,0.001413904,0.0009753751,0.0004664551,0.0005421359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008136627,"about_ca_system_score_gemma":0.001077977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01177881,"about_ca_topic_score_gemma":0.01973043,"domain_scores_codex":[0.997324,0.0009653602,0.0002908702,0.0005721782,0.0007086239,0.0001389234],"domain_scores_gemma":[0.9933091,0.003272504,0.0009920035,0.0009557493,0.001301256,0.0001693575],"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":[0.0007096147,0.0002559463,0.3275874,0.001296941,0.000615363,0.0002289257,0.001833002,0.04115597,0.01270642,0.002723665,0.001993058,0.6088937],"study_design_scores_gemma":[0.0001426523,0.0009662386,0.6172979,0.0007517825,0.001651895,0.0009178687,0.004521878,0.2978636,0.0216035,0.006362113,0.04760978,0.0003106161],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7536709,0.004296668,0.2124474,0.000525372,0.0002000052,0.0004024638,0.004329626,0.006060611,0.01806692],"genre_scores_gemma":[0.8611234,0.001367803,0.1335506,0.00003157754,0.00005592598,0.0002605573,0.001667817,0.0003809766,0.001561232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01177881,"threshold_uncertainty_score":0.03208548,"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."}}