{"id":"W3134559403","doi":"10.3897/aca.4.e64908","title":"Mg-Traits pipeline: advancing functional trait-based approaches in metagenomics","year":2021,"lang":"en","type":"article","venue":"ARPHA Conference Abstracts","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metagenomics; Biology; Trait; Ecology; Ecosystem; Niche; Pipeline (software); Functional diversity; Evolutionary biology; Computational biology; Computer science; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003724363,0.0001648805,0.000224952,0.000037273,0.0001358343,0.00002377666,0.000242993,0.0001467764,0.01075684],"category_scores_gemma":[0.0001298459,0.000176715,0.00006269204,0.0001841229,0.0001832629,0.0001980374,0.0001244509,0.0004361084,0.000415651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008880083,"about_ca_system_score_gemma":0.0001659489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002367387,"about_ca_topic_score_gemma":0.01169843,"domain_scores_codex":[0.9986941,0.0001801791,0.0003068867,0.0003403223,0.0001021264,0.0003763453],"domain_scores_gemma":[0.9993127,0.0002407467,0.00009606286,0.000236605,0.00001794242,0.00009596704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001649221,0.00105853,0.007111216,0.00003919666,0.00003572261,0.0001431441,0.0009006228,0.2047616,0.7508152,0.002229547,0.004484038,0.02825627],"study_design_scores_gemma":[0.0008887567,0.00004964065,0.9568649,0.00001865681,0.00002039734,0.00002751296,0.0003501215,0.00311793,0.02440616,0.00343393,0.01045184,0.0003701691],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722303,0.00003509466,0.001074043,0.000686926,0.0001245614,0.0001129936,0.00001546072,0.00002710868,0.02569358],"genre_scores_gemma":[0.9960184,0.0000076356,0.002058867,0.001344382,0.0000296954,0.00001321437,0.0001547639,0.00001057761,0.0003624344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9497536,"threshold_uncertainty_score":0.9901475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05810056251297208,"score_gpt":0.2379197573801153,"score_spread":0.1798191948671432,"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."}}