{"id":"W2968439511","doi":"10.1109/cibcb.2019.8791491","title":"Large Block Matching Characters for Dehydrin Classification","year":2019,"lang":"en","type":"article","venue":"","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Block (permutation group theory); Computer science; Matching (statistics); Artificial intelligence; Pattern recognition (psychology); Natural language processing; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005694681,0.0003902864,0.000522995,0.001722571,0.0005464296,0.0005852289,0.0005633058,0.000645961,0.004789095],"category_scores_gemma":[0.003254692,0.0001275707,0.0004514974,0.001735306,0.0002913676,0.0007417564,0.0004246724,0.0005652856,0.002397328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003235256,"about_ca_system_score_gemma":0.0005489502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001313015,"about_ca_topic_score_gemma":0.001206668,"domain_scores_codex":[0.9995053,0.000127359,0.00006916242,0.0001198656,0.0001151108,0.00006324502],"domain_scores_gemma":[0.9984804,0.0007516111,0.0001820829,0.0002248602,0.0002744638,0.0000865121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001087141,0.0002898707,0.007055664,0.0002571019,0.00006439621,0.000318654,0.0001773657,0.01536793,0.1516082,0.007473341,0.005065053,0.8112352],"study_design_scores_gemma":[0.00008676479,0.0006795799,0.0163199,0.00006093265,0.00006920772,0.001131749,0.0002464205,0.8446668,0.1010371,0.01476618,0.02086653,0.00006874082],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2066162,0.0006745943,0.7823411,0.0001947745,0.00007745149,0.0003660946,0.002330875,0.003729954,0.003668983],"genre_scores_gemma":[0.4752389,0.0001748244,0.5169911,0.0000645621,0.00004831762,0.0003044249,0.00438827,0.0002371427,0.002552401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004789095,"threshold_uncertainty_score":0.01602107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158031172686035,"score_gpt":0.2569001048990301,"score_spread":0.2453197931721698,"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."}}