{"id":"W3197613652","doi":"10.3390/cells10092300","title":"Quasar: Easy Machine Learning for Biospectroscopy","year":2021,"lang":"en","type":"article","venue":"Cells","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":178,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Light Source (Canada)","funders":"Canadian Institutes of Health Research","keywords":"Scripting language; Computer science; Software; Data science; User Friendly; Machine learning; Human–computer interaction; Data mining; Artificial intelligence; Operating system","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":[0.003775008,0.001644749,0.001090753,0.002142279,0.0007247677,0.002277378,0.003245658,0.00189976,0.02493551],"category_scores_gemma":[0.01112519,0.0009895095,0.001928913,0.001555425,0.0009146725,0.003017942,0.003934009,0.003149579,0.01843168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004338734,"about_ca_system_score_gemma":0.001168591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001196249,"about_ca_topic_score_gemma":0.002227098,"domain_scores_codex":[0.998368,0.0003112655,0.0001533213,0.0003617694,0.0006802498,0.0001254904],"domain_scores_gemma":[0.9964855,0.002021197,0.0002762737,0.000621626,0.0003834178,0.0002119469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007037633,0.0003221741,0.007896515,0.002006563,0.0006513976,0.001077,0.0006255975,0.01380852,0.03476749,0.03127491,0.4524828,0.4543832],"study_design_scores_gemma":[0.0004774085,0.0004552506,0.01151196,0.0004385269,0.0001725628,0.00201275,0.0002346444,0.3037187,0.0610036,0.128922,0.4906403,0.0004123757],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007076792,0.00141555,0.6513289,0.001134636,0.000463092,0.0002017339,0.007791531,0.3260287,0.004558972],"genre_scores_gemma":[0.07735199,0.001649844,0.8364124,0.001926667,0.0004226209,0.0009150557,0.03055584,0.04131671,0.009448792],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02493551,"threshold_uncertainty_score":0.08341759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135565922936155,"score_gpt":0.3122352348393245,"score_spread":0.3008795756099629,"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."}}