{"id":"W2728688957","doi":"10.48550/arxiv.1702.00424","title":"Super-resolution Full Polarimetric Imaging for Radio Interferometry with Sparse Modeling","year":2017,"lang":"en","type":"book-chapter","venue":"MPG.PuRe (Max Planck Society)","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"","keywords":"Remote sensing; Interferometry; Polarimetry; Radar imaging; Superresolution; Computer science; Optics; Geology; Environmental science; Computer vision; Radar; Physics; Telecommunications; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002008212,0.0006838155,0.0008052228,0.0003238606,0.0006563206,0.0002306592,0.0006879295,0.0004123495,0.0002454525],"category_scores_gemma":[0.000008488675,0.0006617146,0.0006400375,0.00009394922,0.0001876255,0.0003433766,0.0001484967,0.0009687142,0.00004084426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002018569,"about_ca_system_score_gemma":0.0002171449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001898313,"about_ca_topic_score_gemma":0.00002247206,"domain_scores_codex":[0.9977372,0.000009928502,0.0004924199,0.0008346987,0.0002521387,0.0006735753],"domain_scores_gemma":[0.9981231,0.00008409054,0.0004785066,0.0009710454,0.0002134379,0.0001298075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004901589,0.0004965828,0.04184948,0.0006072127,0.006629209,0.00002952831,0.0008850395,0.006459915,0.0007258223,0.7598748,0.08146767,0.1004845],"study_design_scores_gemma":[0.006659918,0.0007952561,0.0004031837,0.001244185,0.001756527,0.00005573649,0.0008447696,0.147691,0.0002126719,0.05781671,0.7783748,0.0041453],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00738593,0.002190752,0.8383294,0.0007960069,0.001020512,0.001869947,0.001675279,0.0003891692,0.146343],"genre_scores_gemma":[0.8055328,0.00005354641,0.05292199,0.0001289054,0.003598221,0.0002409541,0.003498851,0.0004632611,0.1335614],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7981469,"threshold_uncertainty_score":0.9995834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02128731526465939,"score_gpt":0.2239278795304594,"score_spread":0.2026405642658,"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."}}