{"id":"W2902057020","doi":"10.1016/j.wasman.2018.11.038","title":"Time-lagged effects of weekly climatic and socio-economic factors on ANN municipal yard waste prediction models","year":2018,"lang":"en","type":"article","venue":"Waste Management","topic":"Municipal Solid Waste Management","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Yard; Lag; Predictive modelling; Mean squared error; Mean absolute error; Engineering; Municipal solid waste; Distributed lag; Statistics; Population; Lag time; Artificial neural network; Mean squared prediction error; Waste management; Mathematics; Computer science; Machine learning","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":[0.001649513,0.0008236106,0.0005732756,0.0002985471,0.0004623683,0.0007786434,0.0009638733,0.001036955,0.002555053],"category_scores_gemma":[0.002789974,0.0005329992,0.0008423119,0.0004044876,0.000318434,0.0007401271,0.000437051,0.001125661,0.0003742613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062712,"about_ca_system_score_gemma":0.001131793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06344195,"about_ca_topic_score_gemma":0.06308489,"domain_scores_codex":[0.9997277,0.00007440568,0.00002431389,0.00007323529,0.00003044261,0.00006999712],"domain_scores_gemma":[0.9980454,0.001416574,0.000106387,0.00008841498,0.0002699465,0.00007327945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004043831,0.0002172417,0.02985703,0.00002409289,0.0001087638,0.00004801383,0.00001891264,0.963306,0.001154794,0.0001620828,0.0002952665,0.004403491],"study_design_scores_gemma":[0.000008927327,0.00004099566,0.007621445,0.000003809839,0.00002920341,0.000003991185,0.0000123147,0.991377,0.0007782192,0.0000588376,0.00005840795,0.00000686036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973894,0.00009893141,0.001696866,0.00006173283,0.0000363117,0.000003382639,0.0003067889,0.00005284006,0.0003538897],"genre_scores_gemma":[0.9984391,0.00004458418,0.0004001472,0.000009670775,0.000005766336,0.000005539252,0.0004307175,0.000005958128,0.0006585221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06344195,"threshold_uncertainty_score":0.1261453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155237440592829,"score_gpt":0.217072992358251,"score_spread":0.2055206179523227,"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."}}