{"id":"W6963953827","doi":"10.25345/c5jn82","title":"MassIVE MSV000087824 - S. erythraea Parallel SIL Metabolomics Data","year":2021,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Identification (biology); Feature (linguistics); Genome; Filter (signal processing)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.000972976,0.001647217,0.002305894,0.0006010432,0.0003188314,0.000563722,0.006869677,0.001279982,0.0136168],"category_scores_gemma":[0.001829484,0.001679157,0.0004576792,0.001087951,0.0005133727,0.0007202298,0.006619153,0.002251491,0.04894641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00035904,"about_ca_system_score_gemma":0.001230318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000870159,"about_ca_topic_score_gemma":0.007632911,"domain_scores_codex":[0.9915007,0.000685919,0.001343784,0.003218324,0.001622771,0.001628498],"domain_scores_gemma":[0.9842247,0.0004909365,0.001175003,0.01317333,0.0003256343,0.0006103528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001167655,0.000440436,0.00002219376,0.000251168,0.001316834,0.001679263,0.00002409946,0.00006849789,0.00005182241,0.00005863716,0.9956749,0.0002953848],"study_design_scores_gemma":[0.001211625,0.00004725432,0.0001058765,0.0002115604,0.001843718,0.0001046372,0.0001739964,0.00009923052,0.00003796903,0.0001643787,0.9942002,0.001799576],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004105255,0.01525232,0.00002205045,0.0001313074,0.002288516,0.0007961448,0.9803616,0.0003226543,0.0007843732],"genre_scores_gemma":[0.000008406901,0.00272496,0.002926743,0.0007909033,0.001716599,0.0001216222,0.9900871,0.0004939796,0.001129679],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0353296,"threshold_uncertainty_score":0.9996275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05314057774676365,"score_gpt":0.3058705817460124,"score_spread":0.2527300039992488,"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."}}