{"id":"W3057319655","doi":"10.3847/1538-4357/abb0e3","title":"A Machine-learning Approach to Integral Field Unit Spectroscopy Observations. I. H ii Region Kinematics","year":2020,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Herzberg Institute of Astrophysics; University of Victoria; Université de Montréal; Centre for Research in Astrophysics of Québec","funders":"","keywords":"Algorithm; Physics; Spectral line; Data cube; Convolutional neural network; Convolution (computer science); Artificial intelligence; Spectral resolution; Spectral density; Galaxy; Filter (signal processing); Field (mathematics); Computer science; Artificial neural network; Mathematics; Astrophysics; Telecommunications; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003332568,0.0003858239,0.0002048474,0.0006478535,0.0002731554,0.0004085004,0.0008382438,0.0005335868,0.001109823],"category_scores_gemma":[0.00110662,0.0003007124,0.0003511124,0.000530643,0.0002603753,0.0006202792,0.0004158064,0.0004332276,0.0002230126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009884575,"about_ca_system_score_gemma":0.0003913478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01331284,"about_ca_topic_score_gemma":0.01522091,"domain_scores_codex":[0.9999136,0.00001980173,0.000003120362,0.00003602614,0.00001678978,0.0000106656],"domain_scores_gemma":[0.9998041,0.00006445844,0.00004454311,0.0000340443,0.00003861357,0.00001421675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007717931,0.00006305376,0.01363032,0.00003637791,0.00005261993,0.00008900202,0.0000528552,0.8140225,0.01662425,0.002426704,0.0009838411,0.1519413],"study_design_scores_gemma":[0.000002021989,0.000005969461,0.001797129,0.000001817025,0.000001539741,0.000007272226,0.000006094697,0.9958863,0.001389874,0.0007293327,0.0001696852,0.000002912789],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5106546,0.0003648241,0.4820687,0.0004872284,0.00003276977,0.00006348728,0.000463063,0.001912928,0.003952475],"genre_scores_gemma":[0.9073639,0.00005718776,0.09114852,0.00004599598,0.00002427994,0.00002627331,0.0002856025,0.00006005423,0.000988343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01331284,"threshold_uncertainty_score":0.02647072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02631733663099023,"score_gpt":0.2305942077678059,"score_spread":0.2042768711368156,"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."}}