{"id":"W1506557292","doi":"10.1002/9780813811048.ch21","title":"Peptidomics for Bioactive Peptide Analysis","year":2010,"lang":"en","type":"other","venue":"","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computational biology; Peptide; Computer science; Biology; Biochemistry","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":[0.0006272592,0.001356338,0.0006426996,0.001426041,0.0002737987,0.001052809,0.0005635639,0.0004367714,0.01365164],"category_scores_gemma":[0.0004532609,0.0002417386,0.0003867762,0.001388864,0.0002245636,0.0009646257,0.0008150996,0.0008187243,0.00554407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003805747,"about_ca_system_score_gemma":0.000310295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006014815,"about_ca_topic_score_gemma":0.001005215,"domain_scores_codex":[0.999793,0.00002630399,0.00001063796,0.00004839969,0.00009736576,0.00002438759],"domain_scores_gemma":[0.9998772,0.0000278022,0.00001838407,0.00002754457,0.00003260919,0.00001640519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003973846,0.0001435078,0.0009545989,0.0007567565,0.00007130451,0.0001920743,0.00002849055,0.002030589,0.5855547,0.009293867,0.01458177,0.3859948],"study_design_scores_gemma":[0.00005380572,0.0001456514,0.006506036,0.0001100556,0.0001104191,0.001083555,0.00005665364,0.02673442,0.7217712,0.01002654,0.2333503,0.00005137841],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.107135,0.03471296,0.6164761,0.001423877,0.0009817438,0.0005049424,0.02395464,0.01158067,0.2032301],"genre_scores_gemma":[0.2740839,0.03318204,0.4913913,0.0007339789,0.0006348606,0.0005839092,0.0268898,0.002417344,0.1700829],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01365164,"threshold_uncertainty_score":0.04566926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009311684858136321,"score_gpt":0.2373307726389413,"score_spread":0.228019087780805,"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."}}