{"id":"W2982532349","doi":"10.1109/apusncursinrsm.2019.8888840","title":"A Multi-Level Reconstruction Algorithm for Electrical Capacitance Tomography Based on Modular Deep Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electrical capacitance tomography; Modular design; Capacitance; Artificial neural network; Computer science; Tomography; Algorithm; Artificial intelligence; Physics; Optics; Electrode","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":[0.00009205757,0.0002475667,0.0002478556,0.0002594198,0.00006142366,0.00003429657,0.0001425631,0.0001770664,0.00004276744],"category_scores_gemma":[0.000008586335,0.0002111846,0.0002693414,0.0009128579,0.00002877989,0.0001120742,0.000003556575,0.0002909197,0.00001110704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000446534,"about_ca_system_score_gemma":0.000005282825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005671277,"about_ca_topic_score_gemma":0.000008519445,"domain_scores_codex":[0.9987097,0.0000210888,0.0002369038,0.000332588,0.0001584757,0.0005411864],"domain_scores_gemma":[0.9994552,0.0001116114,0.00003098737,0.000222936,0.00006270314,0.0001165717],"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":[0.00003935141,0.00008153007,0.002031822,0.00002587363,0.00005191974,0.000001803746,0.000005882875,0.1323178,0.004091781,0.0000788577,0.0001772007,0.8610961],"study_design_scores_gemma":[0.0009192015,0.0003045088,0.002992111,0.00001287776,0.00001498602,0.000007305227,0.00000344881,0.9928088,0.002436797,0.00007402289,0.000105623,0.000320321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04845984,0.0004181227,0.9489961,0.00003204685,0.0006013213,0.0005883588,0.00001536698,0.0005112014,0.0003776344],"genre_scores_gemma":[0.8693584,0.00002499247,0.1299784,0.0002543383,0.0001624702,0.00009438329,0.00001861673,0.00004230591,0.00006611508],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8607758,"threshold_uncertainty_score":0.861186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024615952321527,"score_gpt":0.1969457246333647,"score_spread":0.1866995651101495,"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."}}