{"id":"W4386929454","doi":"10.26434/chemrxiv-2023-77wdm","title":"OptiMS: An Accessible Program for Automating Mass Spectrometry Parameter Optimization and Configuration","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unobservable; Computer science; Metric (unit); Mass spectrometry; Noise (video); Limit (mathematics); Upgrade; Stability (learning theory); Workload; Spectrometer; Signal-to-noise ratio (imaging); SIGNAL (programming language); Algorithm; Simulation; Mathematics; Engineering; Chemistry; Artificial intelligence; Physics; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002308247,0.002655083,0.001209689,0.001309626,0.0007120774,0.001629369,0.002205111,0.0009639996,0.04389527],"category_scores_gemma":[0.003396614,0.001425333,0.001157723,0.0008059657,0.0005893276,0.001588194,0.001646484,0.002596565,0.02386344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007200828,"about_ca_system_score_gemma":0.001720283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001054628,"about_ca_topic_score_gemma":0.00189252,"domain_scores_codex":[0.9992192,0.0001186636,0.0000785215,0.0002367571,0.0002671204,0.0000797074],"domain_scores_gemma":[0.9989366,0.0005775584,0.0001298372,0.0001459281,0.0001463955,0.00006379733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003639255,0.0006911838,0.01010929,0.003671926,0.0008237311,0.0008102584,0.000744048,0.02163253,0.1015834,0.01067074,0.4791555,0.3664681],"study_design_scores_gemma":[0.00146646,0.0005346185,0.01151508,0.0003783109,0.00023787,0.001066918,0.0002398552,0.2759243,0.1793583,0.02611742,0.502647,0.0005137384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.009579877,0.0004652666,0.3371237,0.0002384726,0.0001290771,0.0003879482,0.01541755,0.6293417,0.007316322],"genre_scores_gemma":[0.07666514,0.001493588,0.6528104,0.000917359,0.0001613314,0.003326676,0.05026869,0.1961847,0.0181722],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.04389527,"threshold_uncertainty_score":0.1468443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04114156124631296,"score_gpt":0.3356382943694862,"score_spread":0.2944967331231732,"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."}}