{"id":"W4368404611","doi":"10.1002/cjce.24939","title":"Deep learning‐based hybrid reconstruction algorithm for fibre instance segmentation from <scp>3D</scp> x‐ray tomographic images","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Algorithm; Sample (material); Computer vision; Image segmentation; Outlier; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001651654,0.0001125191,0.0001326882,0.000174145,0.0001424872,0.00009466492,0.0004613744,0.00003887297,0.000001924609],"category_scores_gemma":[0.0001612358,0.0001020865,0.00008547326,0.0005849219,0.00004484117,0.0002905758,0.00001670101,0.0003036228,0.00000327262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279338,"about_ca_system_score_gemma":0.0001152545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005454457,"about_ca_topic_score_gemma":0.00002872977,"domain_scores_codex":[0.9991614,0.00001590307,0.0002475728,0.0001502218,0.0001413489,0.000283611],"domain_scores_gemma":[0.9988719,0.0004600782,0.0001538481,0.0001676842,0.0001141538,0.000232326],"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":[0.000001409759,0.000003703158,0.00008511326,0.0000114145,0.00004084171,0.00001998699,0.0001905512,0.7123595,0.06159962,0.0001761831,0.0005648556,0.2249468],"study_design_scores_gemma":[0.0002323948,0.0000198611,0.000145234,0.00004415985,0.00001162428,0.00004295614,0.00001174522,0.9097736,0.08723795,0.001209712,0.001209741,0.00006096042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07923374,0.0002102721,0.9195772,0.0004809546,0.0002827643,0.0001218313,0.000009623177,0.00007588638,0.000007698663],"genre_scores_gemma":[0.7342942,0.00001407087,0.2651897,0.00008727975,0.0003225903,0.00002952985,0.00001881461,0.00002438235,0.0000193776],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6550605,"threshold_uncertainty_score":0.4162967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00791408704297116,"score_gpt":0.2042455436113731,"score_spread":0.196331456568402,"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."}}