{"id":"W4393600799","doi":"10.5281/zenodo.7706021","title":"Dataset associated with: \"An in silico infrared spectral library of molecular ions for metabolite identification\"","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"In silico; Identification (biology); Metabolite; Computational biology; Chemistry; Infrared; Ion; Biology; Biochemistry; Physics; Botany; Gene; Organic chemistry","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":[0.001232098,0.003073609,0.0022685,0.003019825,0.0009848872,0.002148451,0.003843982,0.004350452,0.0596659],"category_scores_gemma":[0.005442859,0.0006728612,0.001902784,0.004591278,0.0005469577,0.001088182,0.002159826,0.002304773,0.05107343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001707544,"about_ca_system_score_gemma":0.00284005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01284006,"about_ca_topic_score_gemma":0.02486569,"domain_scores_codex":[0.9989313,0.0002048462,0.0001519199,0.0003558477,0.0002360911,0.0001198532],"domain_scores_gemma":[0.9979335,0.0008503325,0.0002477632,0.0003981384,0.0003568007,0.0002134236],"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":[0.0004064821,0.0001226149,0.00191725,0.003551932,0.0001809872,0.0001523182,0.00002472154,0.001679327,0.000800998,0.0008632812,0.9857219,0.004578217],"study_design_scores_gemma":[0.001317065,0.00009238113,0.006192836,0.0007772424,0.0002326893,0.0003006013,0.00007425519,0.002857078,0.001750688,0.003233325,0.9830894,0.00008235682],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002024624,0.0001064385,0.000120322,0.00006495436,0.00001921207,0.00002007824,0.9988754,0.0002334766,0.000357611],"genre_scores_gemma":[0.0006209349,0.00006930363,0.000504238,0.00006179095,0.000004930143,0.00009025159,0.9983847,0.00003344785,0.0002303069],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0596659,"threshold_uncertainty_score":0.1996023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02447649994982811,"score_gpt":0.2611991494425234,"score_spread":0.2367226494926953,"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."}}