{"id":"W2914338618","doi":"10.11606/t.11.2019.tde-17012019-165548","title":"Impactos econômicos da redução do hiato de produtividade da pecuária de corte no Brasil","year":2019,"lang":"pt","type":"dissertation","venue":"","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Deforestation (computer science); Productivity; Greenhouse gas; Agriculture; Computable general equilibrium; Agricultural productivity; Amazon rainforest; Closing (real estate); Land use, land-use change and forestry; Geography; Yield (engineering); Land use; Climate change; Forestry; Production (economics); Agricultural science; Agricultural economics; Environmental science; Economics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003463079,0.0004751078,0.0003160941,0.0004003131,0.0003317736,0.0009828995,0.0005126169,0.0004734396,0.002857235],"category_scores_gemma":[0.001103394,0.0002653947,0.0005114497,0.0005903465,0.0004674615,0.0004573163,0.0003960449,0.0003060256,0.000171591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001984495,"about_ca_system_score_gemma":0.0009211453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1910094,"about_ca_topic_score_gemma":0.1757677,"domain_scores_codex":[0.9998193,0.00003090102,0.00000627809,0.00004263613,0.0000429702,0.00005776491],"domain_scores_gemma":[0.9994358,0.0003141604,0.00005118833,0.00004710952,0.00009729507,0.00005432982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008675694,0.0001479303,0.06243411,0.0003009165,0.0001155505,0.0003537002,0.0001245873,0.8764808,0.02556209,0.002716231,0.0009337119,0.0299628],"study_design_scores_gemma":[0.0001156979,0.0008693894,0.09442199,0.00005376565,0.0002437401,0.0001085878,0.0006032221,0.8789337,0.01877381,0.001897807,0.003921215,0.00005710898],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871699,0.0003243912,0.003100968,0.0001510655,0.00001426805,0.00002718632,0.0006969084,0.0001466005,0.008368794],"genre_scores_gemma":[0.997614,0.0001806652,0.0007540517,0.00001260408,7.549173e-7,0.000006858085,0.0001448403,0.000009821654,0.001276483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1910094,"threshold_uncertainty_score":0.3797953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01532162625827016,"score_gpt":0.2409464506398273,"score_spread":0.2256248243815572,"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."}}