{"id":"W2765688471","doi":"10.5018/economics-ejournal.ja.2018-11","title":"Roads & SDGs, tradeoffs and synergies: learning from Brazil’s Amazon in distinguishing frontiers","year":2018,"lang":"en","type":"article","venue":"Economics","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Amazon rainforest; Deforestation (computer science); Amazonian; Geography; Frontier; Natural resource economics; Environmental planning; Environmental resource management; Economics; Ecology; Computer science","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":[0.001995598,0.0002242495,0.0003271449,0.0005266967,0.0007395546,0.00156089,0.0005373178,0.0004780602,0.004643585],"category_scores_gemma":[0.01689575,0.0001423072,0.0004076648,0.001348177,0.001645011,0.002885476,0.001520566,0.001259346,0.0001621954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002481278,"about_ca_system_score_gemma":0.001508189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06454729,"about_ca_topic_score_gemma":0.1410345,"domain_scores_codex":[0.9994652,0.0002774288,0.00002214152,0.00007555402,0.00007225025,0.00008747794],"domain_scores_gemma":[0.9883967,0.00858525,0.001492603,0.0004172726,0.0006773417,0.0004307729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005949541,0.0005731749,0.6587268,0.000768784,0.0001911805,0.001564823,0.01794467,0.04198321,0.001389985,0.1057637,0.00605329,0.1644454],"study_design_scores_gemma":[0.0001167654,0.0003934236,0.5598869,0.0009831872,0.0003106043,0.0004206779,0.06211035,0.07101167,0.002024018,0.2595971,0.04302443,0.0001209274],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661229,0.0008416849,0.004434245,0.006553946,0.00001719716,0.00002279994,0.0003698747,0.00001587858,0.02162164],"genre_scores_gemma":[0.9984389,0.0001654436,0.0009733419,0.00007390853,0.00000469666,0.000005462158,0.00005100713,0.000003340811,0.0002840164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06454729,"threshold_uncertainty_score":0.1283432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177468071402668,"score_gpt":0.1895798802188677,"score_spread":0.177805199504841,"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."}}