{"id":"W4281941961","doi":"10.36939/ir.202206021141","title":"3D Convolutional Neural Networks for Solving Complex Digital Agriculture and Medical Imaging Problems","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Multispectral image; Computer science; Convolutional neural network; Context (archaeology); Artificial intelligence; Preprocessor; Pattern recognition (psychology); Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007046435,0.0003113546,0.0003899256,0.00008615169,0.0003536137,0.000194304,0.000257024,0.0002537387,0.01260124],"category_scores_gemma":[0.000145453,0.0002616565,0.0001740688,0.000232275,0.00005752286,0.0001373147,0.00007551576,0.0005856326,0.000001189152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001008363,"about_ca_system_score_gemma":0.00007342303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004807489,"about_ca_topic_score_gemma":0.00005966497,"domain_scores_codex":[0.9982929,0.000004262271,0.0003547798,0.0004802569,0.0005014856,0.0003663272],"domain_scores_gemma":[0.9992021,0.0002228287,0.0001957663,0.0001214091,0.000105927,0.0001520119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001229238,0.002293938,0.06519975,0.01095716,0.006238086,0.000227072,0.002613384,0.005310566,0.04263067,0.01128445,0.7520564,0.09995932],"study_design_scores_gemma":[0.003343787,0.00008625491,0.002919164,0.000243359,0.001530614,0.0003528364,0.0152068,0.8991853,0.001929714,0.0008919164,0.0715532,0.002757057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2195403,0.09038248,0.03138531,0.003576196,0.003578933,0.002655029,0.002756983,0.002694377,0.6434304],"genre_scores_gemma":[0.9231596,0.0001704068,0.0002438345,0.0002590192,0.0008857401,0.0002068774,0.03295825,0.00006647091,0.0420498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8938747,"threshold_uncertainty_score":0.9999835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120947945290108,"score_gpt":0.2721233966562288,"score_spread":0.2609139172033277,"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."}}