{"id":"W2035023632","doi":"10.4322/natcon.2013.025","title":"Site Selection for Restoration Planning: A Protocol With Landscape and Legislation Based Alternatives","year":2013,"lang":"en","type":"article","venue":"Natureza & Conservação","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Selection (genetic algorithm); Legislation; Site selection; Protocol (science); Environmental planning; Environmental resource management; Geography; Computer science; Political science; Environmental science; Medicine; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001155418,0.0001058928,0.00009283162,0.00003231584,0.0001421915,0.00009191383,0.00005747494,0.00008304444,0.0002117983],"category_scores_gemma":[0.00001139822,0.0000730458,0.00001577914,0.0001184431,0.00001156562,0.0004992659,0.00001572587,0.00008116696,0.00003812294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002833312,"about_ca_system_score_gemma":0.00001305637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004569838,"about_ca_topic_score_gemma":0.001519397,"domain_scores_codex":[0.9993427,0.00003376159,0.000111388,0.0002226304,0.0001482552,0.0001412537],"domain_scores_gemma":[0.9996786,0.00005735906,0.0001004441,0.00008400329,0.00002902494,0.00005054045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002210603,0.0000322928,0.990772,0.00006332398,0.00001116539,6.727202e-7,0.0003899153,0.001551665,0.003673201,0.00001414386,0.002404535,0.0008660133],"study_design_scores_gemma":[0.002443448,0.0005087333,0.4892271,0.0001004061,0.00002219068,0.00001030016,0.00007408809,0.474414,0.002747875,0.0002716589,0.02988834,0.0002918766],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794025,0.00001345397,0.001213749,0.0004998656,0.00002978357,0.01773673,0.000004855349,0.00005876774,0.001040234],"genre_scores_gemma":[0.9795163,3.203142e-7,0.002060793,0.0003609076,0.00005044694,0.01787437,0.0000243691,0.0000110677,0.0001014459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5015449,"threshold_uncertainty_score":0.2978721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339951478714028,"score_gpt":0.2598956602596826,"score_spread":0.2464961454725423,"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."}}