{"id":"W4283327913","doi":"10.1007/s11356-022-21462-w","title":"Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level","year":2022,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Truck; Environmental science; Population; Geographic information system; Site selection; Municipal solid waste; Environmental engineering; Geography; Waste management; Engineering; Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.004618754,0.0001690096,0.0001810008,0.0006600828,0.002902909,0.0001608563,0.0003005158,0.00004834463,0.0007403144],"category_scores_gemma":[0.00003989481,0.000149782,0.00004464023,0.002339648,0.0007530136,0.0006632704,0.0006652811,0.0003375744,0.0001452538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001325841,"about_ca_system_score_gemma":0.00004041654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004066851,"about_ca_topic_score_gemma":0.00003681214,"domain_scores_codex":[0.9958382,0.000306726,0.000288035,0.0004994996,0.002308582,0.0007589607],"domain_scores_gemma":[0.9991022,0.00006342974,0.00011269,0.0002655213,0.000008287722,0.0004478958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00053742,0.0003218485,0.8928704,0.00003910592,0.0001071574,0.00001968252,0.007258313,0.0278198,0.0217938,0.004829169,0.008535697,0.03586759],"study_design_scores_gemma":[0.0003042504,0.0004245937,0.9414679,0.00001564958,0.00002395646,0.00002147318,0.00181583,0.004398445,0.0002162131,0.00006943215,0.05100446,0.0002377852],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855681,0.0001096949,0.0008710654,0.007348322,0.00008305167,0.0007054667,0.00005675179,0.00003784089,0.005219681],"genre_scores_gemma":[0.9967194,0.0001724689,0.0009153381,0.001551866,0.00004716505,0.00007821594,0.00004900948,0.000008239014,0.000458364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0485975,"threshold_uncertainty_score":0.9983952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03293338347613409,"score_gpt":0.2863985353383399,"score_spread":0.2534651518622058,"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."}}