{"id":"W2973973237","doi":"10.48075/igepec.v23i0.22753","title":"DO DESENVOLVIMENTO REGIONAL AO DESENVOLVIMENTO TERRITORIAL: UMA COMPARAÇÃO QUÉBEC - BRASIL (1960-2010)","year":2019,"lang":"pt","type":"article","venue":"Informe GEPEC","topic":"Regional Development and Policy","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Political science; Regional development; Humanities; Geography; Regional science; Philosophy","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":[0.0009525987,0.0002626011,0.0003476191,0.004498999,0.002648778,0.002408101,0.0005130324,0.0004859491,0.007549831],"category_scores_gemma":[0.00243256,0.0001577398,0.0003044513,0.01135199,0.0027014,0.001059526,0.001203539,0.0006309361,0.0002491648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0341861,"about_ca_system_score_gemma":0.02285606,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9747313,"about_ca_topic_score_gemma":0.9927554,"domain_scores_codex":[0.9992078,0.0001371758,0.00003110825,0.00013212,0.0002328284,0.0002589287],"domain_scores_gemma":[0.9981784,0.0002958553,0.0003042346,0.0001069504,0.0008002575,0.000314351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003198117,0.00007923382,0.4281321,0.002011011,0.0003352726,0.001433761,0.05583266,0.002455988,0.001675733,0.1759817,0.06368463,0.268058],"study_design_scores_gemma":[0.000004756701,0.00002623033,0.8648912,0.0004360335,0.0000842852,0.0001567642,0.01596057,0.0003329334,0.0001957215,0.001006828,0.1168786,0.0000261529],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6840302,0.05186087,0.002062422,0.01479512,0.0003168584,0.0001151357,0.007783688,0.00009518007,0.2389405],"genre_scores_gemma":[0.9714363,0.01081212,0.0007949425,0.0004729165,0.00004168113,0.00002873403,0.000877814,0.00002239271,0.01551298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0341861,"threshold_uncertainty_score":0.2480387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06823072737335768,"score_gpt":0.3309899190658672,"score_spread":0.2627591916925095,"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."}}