{"id":"W4393371854","doi":"10.1016/j.jwpe.2024.105187","title":"Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Water Process Engineering","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Sultan Qaboos University","keywords":"Water quality; Feature (linguistics); Quality (philosophy); Computer science; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.00116638,0.0002034095,0.0003779392,0.0004056415,0.00005449502,0.0001990678,0.0002920958,0.0001256252,0.00001813546],"category_scores_gemma":[0.0000412218,0.0001307914,0.0001014431,0.0004144027,0.00004119441,0.001335073,0.0002391283,0.0007210715,0.000002756961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000279572,"about_ca_system_score_gemma":0.000004395007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007462792,"about_ca_topic_score_gemma":0.000007538976,"domain_scores_codex":[0.9983628,0.00004266875,0.0005256277,0.0002787497,0.0003601043,0.000430028],"domain_scores_gemma":[0.9996415,0.00003454548,0.000067462,0.0001587811,0.00002419412,0.00007356611],"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":[0.00001400318,0.000009356203,0.1476949,0.0001739368,0.0001161691,0.00007380648,0.005554908,0.776097,0.0700039,0.000002241344,0.000001097099,0.0002586491],"study_design_scores_gemma":[0.0002697922,0.00006783096,0.01379607,0.0005427927,0.0002292718,0.0001063385,0.001221424,0.6448296,0.3366466,0.001577473,0.0001830035,0.0005297535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858059,0.000531395,0.0130901,0.0001832915,0.000220242,0.00005388013,4.438413e-7,0.0000923174,0.00002244565],"genre_scores_gemma":[0.9938839,0.00004338712,0.005888571,0.000002233716,0.00008681706,0.000004327605,8.560224e-7,0.00002764216,0.00006227585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2666427,"threshold_uncertainty_score":0.5333518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02929946912552182,"score_gpt":0.2808161773458204,"score_spread":0.2515167082202985,"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."}}