{"id":"W3006003209","doi":"10.1021/acs.cgd.9b01482","title":"Simultaneous Measurement of Solution Concentration and Slurry Density by Raman Spectroscopy with Artificial Neural Network","year":2020,"lang":"en","type":"article","venue":"Crystal Growth & Design","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Partial least squares regression; Artificial neural network; Mean squared error; Principal component regression; Principal component analysis; Biological system; Raman spectroscopy; Analytical Chemistry (journal); Linear regression; Chemistry; Mathematics; Artificial intelligence; Statistics; Chromatography; Computer science; Optics; 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":[0.0001466974,0.000205732,0.0003084126,0.00001437652,0.0001606284,0.00005173196,0.0001053529,0.00009798985,0.00009945509],"category_scores_gemma":[0.0001740469,0.0001892866,0.00004820025,0.0003401589,0.0001542721,0.00009845733,0.00002636369,0.0001781493,0.000001813272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008781092,"about_ca_system_score_gemma":0.00006291448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003380377,"about_ca_topic_score_gemma":0.000005102223,"domain_scores_codex":[0.9985802,0.00004809871,0.0002857849,0.0003464415,0.0004175903,0.0003219161],"domain_scores_gemma":[0.9992583,0.0001141398,0.0001818594,0.0001208037,0.0001630215,0.0001618556],"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":[0.0007493255,0.00006517804,0.002424965,0.00009046263,0.00009330544,0.00001549343,0.0001570106,0.001110793,0.993523,0.00009169288,0.001535965,0.0001428329],"study_design_scores_gemma":[0.000484787,0.0003528154,0.0000777513,0.0000192241,0.0002615261,0.000009578261,0.0001475681,0.02605349,0.9720688,0.0002523019,0.00004120508,0.0002309496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.712042,0.0011362,0.2856157,0.0003712517,0.00003081899,0.0001891464,0.00001348517,0.0001076861,0.0004936203],"genre_scores_gemma":[0.9976795,0.0000276635,0.001897276,0.0001245519,0.0002114291,0.00000553996,0.00002019921,0.00001978125,0.00001406802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2856374,"threshold_uncertainty_score":0.7718884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02659223818816554,"score_gpt":0.2277656582285398,"score_spread":0.2011734200403743,"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."}}