{"id":"W4312720629","doi":"10.1007/978-3-031-17531-2_4","title":"Relabeling Metabolic Pathway Data with Groups to Improve Prediction Outcomes","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scalability; Inference; Metabolic pathway; Set (abstract data type); Clinical pathway; Artificial intelligence; Computational biology; Biology; Genetics; Gene; Engineering","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":[0.001969309,0.001574344,0.00118833,0.002242098,0.0006959567,0.001342037,0.001520518,0.0009429852,0.004115228],"category_scores_gemma":[0.004612091,0.0003389172,0.001862572,0.002608901,0.0003608851,0.001872028,0.001660508,0.00185874,0.003757489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005614978,"about_ca_system_score_gemma":0.001095165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00721536,"about_ca_topic_score_gemma":0.01254129,"domain_scores_codex":[0.9989545,0.0002187217,0.00006481922,0.0004023355,0.0002333028,0.0001263074],"domain_scores_gemma":[0.9969895,0.001266703,0.0001868489,0.0009141759,0.0004859914,0.0001567853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001353963,0.0008746989,0.03048934,0.0004177552,0.0004728728,0.0002490176,0.0001562062,0.06929079,0.02119596,0.002162441,0.05459906,0.8187379],"study_design_scores_gemma":[0.0001252141,0.0005273797,0.009360622,0.000127906,0.0003738042,0.0002488791,0.0003579311,0.8856972,0.03825808,0.03790428,0.02692383,0.00009488558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2007896,0.002495772,0.7382878,0.001242164,0.001158641,0.0003844929,0.02229454,0.02663886,0.006708046],"genre_scores_gemma":[0.4511257,0.000850334,0.4681503,0.0005951281,0.0004006206,0.0002981297,0.06880973,0.001461405,0.008308702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00721536,"threshold_uncertainty_score":0.01434672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022498260358813,"score_gpt":0.2199031045769683,"score_spread":0.2096781219733802,"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."}}