{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001340258,0.0001073918,0.0001163381,0.00001813128,0.00007436333,0.00002385476,0.0001758877,0.0001542425,0.0002603691],"category_scores_gemma":[0.0002078329,0.00009772636,0.0001109856,0.0001013393,0.00009398907,0.000001220243,0.0001387909,0.0001573603,0.00002070745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001796805,"about_ca_system_score_gemma":0.00007365432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000393205,"about_ca_topic_score_gemma":0.000002573545,"domain_scores_codex":[0.9990408,0.0000289453,0.0001290715,0.0003386955,0.0001430441,0.000319396],"domain_scores_gemma":[0.9994974,0.00002947995,0.00002724904,0.0002356444,0.00008786592,0.0001223692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004403884,0.00006180284,0.0002078106,0.00002545889,0.00001744983,0.000004444258,0.000002942556,6.928763e-7,0.9864101,0.0002013929,0.01233336,0.0006904971],"study_design_scores_gemma":[0.000171607,0.0001569165,0.000007848518,0.000005112624,0.000003605654,0.00000337058,0.000004724086,0.0000864258,0.6169794,0.0003660446,0.3821374,0.00007756348],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6365438,0.01710652,0.2618289,0.00333093,0.001148565,0.001196413,0.0001983799,0.0003719129,0.07827457],"genre_scores_gemma":[0.8798962,0.003273214,0.04340705,0.001466822,0.001063385,0.0001546792,0.001155626,0.00008746437,0.06949562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.369804,"threshold_uncertainty_score":0.3985165,"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."}}