{"id":"W4404769381","doi":"10.1016/j.dche.2024.100202","title":"Exploring spatial and temporal importance of input features and the explainability of machine learning-based modelling of water distribution systems","year":2024,"lang":"en","type":"article","venue":"Digital Chemical Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Distribution (mathematics); Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007931885,0.0005140381,0.00021785,0.0003075281,0.0002638683,0.0008494532,0.0004835593,0.0004668385,0.000429875],"category_scores_gemma":[0.004553515,0.000283349,0.0003337968,0.0002081556,0.000504975,0.0008211581,0.0004414822,0.0008961837,0.00004006679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001746819,"about_ca_system_score_gemma":0.001105273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1100735,"about_ca_topic_score_gemma":0.07065433,"domain_scores_codex":[0.9997874,0.00007867967,0.00001186873,0.00005428434,0.0000337144,0.00003398051],"domain_scores_gemma":[0.9979937,0.00155512,0.0001919638,0.00008319286,0.0001481744,0.00002780901],"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":[0.00002798332,0.00001175649,0.006728895,0.0000147865,0.00001879661,0.00004528711,0.00007615139,0.9861522,0.001002089,0.0007643847,0.00004880038,0.00510889],"study_design_scores_gemma":[7.884126e-7,0.000004839248,0.001163963,0.000001501577,0.000002240551,0.000002929688,0.00001140643,0.9981452,0.0002233362,0.0004032722,0.0000388636,0.000001761044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8742533,0.000153325,0.1228089,0.0006657843,0.00001351079,0.00002673782,0.0001742654,0.0002320109,0.001672125],"genre_scores_gemma":[0.9960514,0.00002671313,0.003635156,0.00001024227,0.000002305179,0.000004806913,0.00004199437,0.000007415855,0.0002201886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1100735,"threshold_uncertainty_score":0.2188656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130145493800979,"score_gpt":0.1665687759123022,"score_spread":0.1535542265322043,"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."}}