{"id":"W6929341690","doi":"10.48550/arxiv.1809.10888","title":"Real-time Adaptive Optics with pyramid wavefront sensors: Accurate wavefront reconstruction using iterative methods","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adaptive optics and wavefront sensing","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wavefront; Adaptive optics; Pyramid (geometry); Wavefront sensor; Context (archaeology); Inverse problem; Iterative method; Conjugate gradient method","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004057421,0.0009221303,0.0009993883,0.0003019935,0.0005179998,0.000205433,0.0004189968,0.0003740433,0.0002460821],"category_scores_gemma":[0.00001302064,0.0009294307,0.0003645238,0.0003552615,0.0006916777,0.0004954473,0.000763897,0.0009480497,0.00007853303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005820889,"about_ca_system_score_gemma":0.0004364149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008689327,"about_ca_topic_score_gemma":0.0000176852,"domain_scores_codex":[0.9964105,0.0005429188,0.0004978044,0.001635145,0.000155745,0.0007578256],"domain_scores_gemma":[0.9966111,0.0001929179,0.001017826,0.0009944815,0.0008654897,0.0003181729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00179963,0.0004927838,0.002653371,0.0001107982,0.004683088,0.0004375166,0.002921146,0.8877369,0.007324194,0.08642014,0.0002228524,0.005197558],"study_design_scores_gemma":[0.0009233677,0.0002727666,0.0002099692,0.0004974981,0.0007789354,0.00001736071,0.002273301,0.965685,0.002695861,0.02529964,0.0000663676,0.001279903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5087489,0.00000928357,0.4772615,0.00001256998,0.0004138802,0.0005627924,0.000168617,0.00009085385,0.01273162],"genre_scores_gemma":[0.8028867,0.00003238565,0.1938911,0.000009746579,0.0007033641,0.000001387376,0.0001177892,0.0001101645,0.002247299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2941378,"threshold_uncertainty_score":0.9993156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07238355170596596,"score_gpt":0.2420452590580789,"score_spread":0.1696617073521129,"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."}}