{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005276914,0.000925682,0.0005799974,0.0005981287,0.0002111861,0.0007412124,0.0008752679,0.001371486,0.00161579],"category_scores_gemma":[0.00155742,0.0004462897,0.0007193863,0.000752877,0.0004550447,0.0006670392,0.0007354965,0.001028715,0.0003790438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009390506,"about_ca_system_score_gemma":0.0008809227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01335804,"about_ca_topic_score_gemma":0.01521868,"domain_scores_codex":[0.9998285,0.00003282733,0.00001167613,0.00005399685,0.00004548491,0.00002745234],"domain_scores_gemma":[0.9996322,0.0002047246,0.00004846904,0.00003028172,0.00006573577,0.00001845452],"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":[0.00006867088,0.00004576815,0.001210561,0.0001251009,0.00007138382,0.00009174627,0.00002425261,0.8953897,0.003705057,0.003677903,0.002015166,0.0935747],"study_design_scores_gemma":[0.000001718503,0.000006170676,0.0001127117,0.000004320737,0.000004013731,0.000007601151,0.000002559991,0.9982226,0.0003507693,0.0009842701,0.0003012508,0.000001936831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06816631,0.00335768,0.9215918,0.0009052031,0.0001407548,0.00007560921,0.0006733198,0.001355587,0.003733687],"genre_scores_gemma":[0.6452978,0.003326146,0.3408942,0.0005515648,0.0001713675,0.0002556198,0.002356033,0.0001625784,0.006984864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01335804,"threshold_uncertainty_score":0.02656054,"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."}}