{"id":"W6888970811","doi":"10.25345/c5rv0d39m","title":"MassIVE MSV000088996 - Qtof_DDA_NEG_RAW_CS_metabolomics_annotation_example_set","year":2022,"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); Process (computing); Work (physics); Set (abstract data type)","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008605878,0.001164428,0.001345189,0.001242506,0.0006681744,0.0002807324,0.002159684,0.0005299724,0.5342217],"category_scores_gemma":[0.0007794588,0.001300043,0.0005709808,0.001293911,0.0003102971,0.0002325668,0.001150654,0.002030867,0.02196795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005755239,"about_ca_system_score_gemma":0.0005240726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006809729,"about_ca_topic_score_gemma":0.000988522,"domain_scores_codex":[0.9934041,0.0007181451,0.001177053,0.001686796,0.001796493,0.001217447],"domain_scores_gemma":[0.9946497,0.0004760986,0.001378126,0.002788465,0.0002673668,0.0004402304],"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":[0.0001109959,0.0002749465,0.00001113496,0.00008465422,0.0003611131,0.0003692862,0.00005592875,0.0001497787,0.0001162266,0.0003899676,0.9977257,0.0003502632],"study_design_scores_gemma":[0.0009813547,0.0001345416,0.0001484633,0.00002623419,0.0003997798,0.00004076199,0.0001681243,0.00002178638,0.00003889484,0.0006918361,0.9959784,0.001369867],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001268538,0.001096557,0.0000046503,0.0001040073,0.003031456,0.0009386184,0.9929117,0.0003202509,0.001465931],"genre_scores_gemma":[0.0000278989,0.0002863063,0.0002086754,0.001255827,0.0007033235,0.0004352051,0.9954144,0.0003766248,0.001291784],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5122538,"threshold_uncertainty_score":0.9989449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02385881946084551,"score_gpt":0.2785485295547287,"score_spread":0.2546897100938832,"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."}}