{"id":"W3098828765","doi":"","title":"Analysis of ecological land use change and impacting factors in Toronto.","year":2017,"lang":"en","type":"article","venue":"Shanghai Land and Resources","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Geography; Land use; Land use, land-use change and forestry; Ecology; Climate change; Environmental resource management; Environmental science; Biology","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":[0.0001810008,0.0002204688,0.0001761778,0.001083249,0.0008435678,0.0006305312,0.000348691,0.0001779096,0.003318538],"category_scores_gemma":[0.0008226822,0.0001225704,0.0003287575,0.00366185,0.0003736934,0.0002380035,0.0006178902,0.0002704526,0.0001839246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01519677,"about_ca_system_score_gemma":0.007868535,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9728854,"about_ca_topic_score_gemma":0.9890892,"domain_scores_codex":[0.9998293,0.00002815297,0.00001201645,0.0000244253,0.00003812495,0.00006790883],"domain_scores_gemma":[0.9995435,0.00005523978,0.00007511105,0.00001888752,0.0001608033,0.0001464548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001721888,0.00003220805,0.981257,0.00009282913,0.000126482,0.0004511057,0.001991685,0.002944898,0.0006872809,0.0009011911,0.002324431,0.009018618],"study_design_scores_gemma":[0.000001265946,0.000009763735,0.9968637,0.000006123563,0.00001681152,0.00001858134,0.001390855,0.0007569489,0.0000721042,0.00002402425,0.000836883,0.000003006204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932408,0.0004209857,0.0001831573,0.0001574293,0.000005015058,0.00001301183,0.003967815,0.000008807739,0.002003061],"genre_scores_gemma":[0.9973901,0.0001431202,0.0001022252,0.00001057586,0.000001882839,0.000005723086,0.001364272,0.000002288399,0.0009797857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02711463,"threshold_uncertainty_score":0.1102608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03388986218726745,"score_gpt":0.2595265969708692,"score_spread":0.2256367347836017,"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."}}