{"id":"W2806170295","doi":"10.1177/0003702818778031","title":"Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples","year":2018,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Preprocessor; Hyperspectral imaging; Computer science; Data pre-processing; Quality (philosophy); Figure of merit; Artificial intelligence; Pattern recognition (psychology); Data mining; Computer vision; Physics","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":[0.001229663,0.0002351302,0.0004306667,0.00007134589,0.0001480411,0.00003170304,0.0003534116,0.0003751775,0.00004494802],"category_scores_gemma":[0.0009339366,0.0001871318,0.00008748272,0.0002222194,0.001461874,0.000003560314,0.0001895883,0.0001583749,0.000001832443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004432315,"about_ca_system_score_gemma":0.0001635759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001104403,"about_ca_topic_score_gemma":0.000001969071,"domain_scores_codex":[0.9979443,0.00009610711,0.000502689,0.0006905408,0.0002133011,0.000553058],"domain_scores_gemma":[0.9988611,0.0003133837,0.0001593256,0.000341533,0.0001388066,0.000185845],"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":[0.0006493797,0.00008256183,0.0002853024,0.0000832837,0.00006386167,2.571306e-7,0.00002217143,2.590596e-8,0.9840629,0.009040736,0.0006312794,0.005078251],"study_design_scores_gemma":[0.001008118,0.0008472282,0.0008334997,0.00002474385,0.00001511991,0.000004566061,0.00003573189,0.0002006078,0.9680863,0.01576859,0.01293936,0.000236138],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2537868,0.0003185455,0.7440878,0.0003182015,0.00005123481,0.0004583884,0.00003074062,0.0001440592,0.0008043406],"genre_scores_gemma":[0.5340645,0.0001180304,0.4650935,0.000247765,0.0002732564,0.00009774541,0.00007288712,0.00001792876,0.0000143653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2802778,"threshold_uncertainty_score":0.7631012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05012445120445311,"score_gpt":0.4577306327730596,"score_spread":0.4076061815686065,"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."}}