{"id":"W2949214535","doi":"10.1016/j.resconrec.2019.06.013","title":"A data driven technique applying GIS, and remote sensing to rank locations for waste disposal site expansion","year":2019,"lang":"en","type":"article","venue":"Resources Conservation and Recycling","topic":"Municipal Solid Waste Management","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Polygon (computer graphics); Municipal solid waste; Land cover; Weighting; Standard deviation; Remote sensing; Support vector machine; Land use; Computer science; Engineering; Civil engineering; Waste management; Geography; Statistics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006186509,0.0006801251,0.0005899416,0.003594648,0.0006907775,0.001038171,0.001112379,0.0006776853,0.004056453],"category_scores_gemma":[0.00232001,0.0004898545,0.0007018683,0.003077884,0.0003314104,0.0007710944,0.001073231,0.0005745345,0.001330599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004368382,"about_ca_system_score_gemma":0.0015998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009109165,"about_ca_topic_score_gemma":0.0228776,"domain_scores_codex":[0.9994045,0.00006879038,0.00004420262,0.000135133,0.0002868662,0.00006047108],"domain_scores_gemma":[0.9988238,0.0003649981,0.0000948071,0.0001555776,0.0004909466,0.0000698977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003319443,0.0006705473,0.01936086,0.000162583,0.0001131175,0.0002724371,0.0003075991,0.05053125,0.05401891,0.00516624,0.008888125,0.8601763],"study_design_scores_gemma":[0.00005931969,0.0002220064,0.008845132,0.00002496346,0.00005577939,0.0003778801,0.0004013354,0.9374858,0.03716382,0.005150234,0.01015164,0.00006208847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05654948,0.00006918039,0.9317554,0.0001855724,0.00007774319,0.0003789114,0.002559239,0.005441472,0.002982875],"genre_scores_gemma":[0.1952419,0.00004300373,0.7992713,0.00007847558,0.0000267755,0.000241726,0.002389841,0.000121261,0.002585706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009109165,"threshold_uncertainty_score":0.0181123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02952991482862127,"score_gpt":0.2736569366328694,"score_spread":0.2441270218042481,"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."}}