{"id":"W4389422261","doi":"10.48550/arxiv.2312.02908","title":"Deep Learning Segmentation of Spiral Arms and Bars","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Astronomical Observations and Instrumentation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; University of Hertfordshire","keywords":"Spiral (railway); Bar (unit); Segmentation; Artificial intelligence; Crowdsourcing; Computer science; Deep learning; Market segmentation; Computer vision; Physics; Engineering; Mechanical engineering; Business; Marketing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000564949,0.001168689,0.0005234892,0.001118808,0.000432879,0.001605933,0.001385226,0.001688351,0.003345669],"category_scores_gemma":[0.002767481,0.0005657861,0.0009355904,0.0007302006,0.000631721,0.001316222,0.001438532,0.001364396,0.002771456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009039083,"about_ca_system_score_gemma":0.000840231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005531011,"about_ca_topic_score_gemma":0.007873499,"domain_scores_codex":[0.999549,0.00005651556,0.0000192086,0.0001914086,0.0000940206,0.00008985037],"domain_scores_gemma":[0.9991274,0.000226599,0.0001282973,0.0001999697,0.0002310616,0.00008662276],"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":[0.0007581163,0.0001651395,0.0139486,0.0004214829,0.000167373,0.0003316414,0.0004300079,0.2829711,0.05580311,0.01432044,0.03268489,0.5979981],"study_design_scores_gemma":[0.00002563393,0.00009800687,0.003176104,0.00008608655,0.00003100839,0.0001195652,0.00009877178,0.9447008,0.01921566,0.02192775,0.01049403,0.00002650941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1787523,0.001684633,0.7860557,0.00111088,0.000246273,0.0001688812,0.003090168,0.01320479,0.0156864],"genre_scores_gemma":[0.6732888,0.0005281643,0.3024919,0.0005417991,0.0001235536,0.0001269153,0.008589545,0.001028146,0.01328111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005531011,"threshold_uncertainty_score":0.01119238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05714293221053437,"score_gpt":0.1712588250683917,"score_spread":0.1141158928578573,"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."}}