{"id":"W2883329982","doi":"10.3390/jsan7030029","title":"Trajectory-Assisted Municipal Agent Mobility: A Sensor-Driven Smart Waste Management System","year":2018,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Computer science; Truck; Greedy algorithm; Cloud computing; Integer programming; Smart city; Trajectory; Set (abstract data type); Waste collection; Software deployment; Real-time computing; Internet of Things; Computer security; Municipal solid waste; Automotive engineering; Engineering; Operating system","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":[0.0002920929,0.0004871188,0.0005215831,0.0002759894,0.0008663825,0.0006498723,0.001199323,0.0007014865,0.001624471],"category_scores_gemma":[0.0005400486,0.0002087758,0.0003566399,0.000552625,0.0003335235,0.0007579308,0.001310283,0.0003778579,0.0004635018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006333819,"about_ca_system_score_gemma":0.001305109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005847371,"about_ca_topic_score_gemma":0.006272125,"domain_scores_codex":[0.99984,0.00003138632,0.00001268128,0.00004403814,0.00003308253,0.00003880848],"domain_scores_gemma":[0.9997887,0.000035992,0.00004182259,0.00003581683,0.00004163229,0.00005604065],"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":[0.0005963024,0.0003968256,0.005427632,0.0001607881,0.00008903239,0.0008981029,0.0004837656,0.7902727,0.03427846,0.01617148,0.005187959,0.146037],"study_design_scores_gemma":[0.00002687079,0.00007543685,0.0003801275,0.000004830903,0.00001693622,0.00005793069,0.00008594865,0.9918202,0.002613444,0.001509881,0.003396126,0.00001231665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2112137,0.0002763991,0.7734125,0.001104261,0.0001522299,0.0002894564,0.0003243094,0.003288832,0.009938295],"genre_scores_gemma":[0.941151,0.000108325,0.05572074,0.00007132583,0.00001697089,0.0001325969,0.0001681053,0.00002754669,0.002603382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005847371,"threshold_uncertainty_score":0.01162666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618730083049197,"score_gpt":0.231710230898665,"score_spread":0.215522930068173,"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."}}