{"id":"W2974467107","doi":"10.3847/1538-4357/ab4657","title":"CLOVER: Convnet Line-fitting Of Velocities in Emission-line Regions","year":2019,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Herzberg Institute of Astrophysics; University of Alberta; University of Victoria","funders":"","keywords":"Line (geometry); Pixel; Noise (video); Physics; Artificial intelligence; Spectral line; Emission spectrum; Algorithm; Pattern recognition (psychology); Computer science; Astrophysics; Mathematics; Geometry; Image (mathematics); Astronomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001091162,0.0001024985,0.0001960091,0.00002596986,0.00009064809,0.00002482534,0.0003100299,0.00004639498,0.0004845361],"category_scores_gemma":[0.00003312424,0.00006983553,0.0001083587,0.0001277014,0.00009442443,0.0000633638,0.00005878816,0.0006004781,0.00004456906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000412724,"about_ca_system_score_gemma":0.00007191254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001885948,"about_ca_topic_score_gemma":0.000001039768,"domain_scores_codex":[0.9991457,0.00002640969,0.0003022173,0.0001132531,0.0002043912,0.0002080553],"domain_scores_gemma":[0.9992341,0.0001862248,0.0001859167,0.0002723182,0.00005176954,0.00006968216],"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":[0.0001035563,0.0001666436,0.001422453,0.0000219813,0.00003467502,0.000004190114,0.0003971553,0.001326429,0.9867382,0.007961109,0.0006249452,0.001198632],"study_design_scores_gemma":[0.003423992,0.0003397823,0.008415654,0.000680658,0.0001140021,0.0001712926,0.004798092,0.02369607,0.9237882,0.02847106,0.005599407,0.0005017322],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948701,0.0000520889,0.001448367,0.001052084,0.00004572692,0.00004348459,0.00001052421,0.00001440647,0.002463272],"genre_scores_gemma":[0.9976424,0.00002983389,0.0005702182,0.0000577561,0.0004911084,0.000004427425,0.000005140743,0.00001297628,0.001186128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06294997,"threshold_uncertainty_score":0.530533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167419980379153,"score_gpt":0.2622985744105901,"score_spread":0.2506243746067985,"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."}}