{"id":"W4402502922","doi":"10.48550/arxiv.2408.08949","title":"Demonstration of hybrid foreground removal on CHIME data","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Office of Science; Alliance de recherche numérique du Canada; Industry Canada; Canada Research Chairs; National Research Council Canada; University of Toronto; University of Utah; Smithsonian Astrophysical Observatory; Western Canada Research Grid; Canadian Institute for Advanced Research; Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; Alfred P. Sloan Foundation; McGill University; U.S. Department of Energy; Government of Canada; National Science Foundation","keywords":"Computer science; Geology","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":[0.0009956292,0.0006603921,0.0004116302,0.001052938,0.0004127841,0.0008126291,0.0006549556,0.0008012452,0.002346742],"category_scores_gemma":[0.001391072,0.0003080131,0.0004931979,0.0008673721,0.0003779749,0.0004585012,0.001069383,0.0008266735,0.00123974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002001911,"about_ca_system_score_gemma":0.0003296118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002310592,"about_ca_topic_score_gemma":0.005827378,"domain_scores_codex":[0.9995617,0.00005928026,0.00001926561,0.00009483598,0.0001716082,0.00009321498],"domain_scores_gemma":[0.9994123,0.0001297563,0.00004438638,0.0001923632,0.0001462997,0.00007486721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001527568,0.0002834692,0.03468461,0.0004126344,0.0005709597,0.002868187,0.001015096,0.02205014,0.7255099,0.003098007,0.01241271,0.1955667],"study_design_scores_gemma":[0.0003007564,0.0003432827,0.2012983,0.00009486111,0.0002167094,0.002351311,0.0004808036,0.2603493,0.4837152,0.005222925,0.04538368,0.0002429375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8045097,0.0006465184,0.1551959,0.00122148,0.0002442191,0.0001355502,0.004423737,0.01859774,0.01502523],"genre_scores_gemma":[0.7528134,0.0001747653,0.2364081,0.0004572796,0.00008350106,0.00004834247,0.006782835,0.001246319,0.001985506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002346742,"threshold_uncertainty_score":0.007850647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1271005896729133,"score_gpt":0.2413632924819403,"score_spread":0.114262702809027,"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."}}