{"id":"W3208518250","doi":"10.20944/preprints202111.0078.v1","title":"GRaVN: A Convolutional Network Approach to Generalised Characterisation of Raman Spectra for Space Exploration","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Convolutional neural network; Computer science; Raman spectroscopy; Quality assurance; Artificial neural network; Artificial intelligence; Automation; Quality (philosophy); Process (computing); Data mining; Pattern recognition (psychology); Engineering; 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":[0.001246008,0.00112804,0.0004508553,0.00113179,0.0003218367,0.0008734746,0.001649694,0.0009565486,0.002023605],"category_scores_gemma":[0.002623891,0.0004911754,0.0008615367,0.0006815428,0.0006184761,0.00105664,0.001579352,0.001350546,0.001002079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009340406,"about_ca_system_score_gemma":0.0007674328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01124722,"about_ca_topic_score_gemma":0.01956143,"domain_scores_codex":[0.9996248,0.00007098892,0.00002045893,0.0001345839,0.00009941297,0.00004969641],"domain_scores_gemma":[0.9993615,0.000215342,0.00008942562,0.0001480364,0.0001554585,0.00003024097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003318409,0.0001376606,0.004470007,0.0003065514,0.0003219568,0.0003337446,0.0001799147,0.3815188,0.05504198,0.01307239,0.01142405,0.5328611],"study_design_scores_gemma":[0.00000600274,0.00004521762,0.001018392,0.00002341999,0.00001836013,0.00007808577,0.00002012718,0.9799589,0.009203727,0.006248834,0.003362138,0.00001679389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02669039,0.0005609553,0.9621604,0.0002236874,0.00007710205,0.0001026047,0.0009514156,0.007195692,0.002037811],"genre_scores_gemma":[0.3929108,0.0007650953,0.5848716,0.0003968601,0.00007320655,0.0002713855,0.007160541,0.001005668,0.01254475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01124722,"threshold_uncertainty_score":0.02236348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1217343183116862,"score_gpt":0.3829144887996011,"score_spread":0.2611801704879149,"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."}}