{"id":"W2805405695","doi":"10.1117/12.2314243","title":"Long-term monitoring of the throughput in Las Cumbres Observatory's fleet of telescopes","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Adaptive optics and wavefront sensing","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Los Alamos National Laboratory; Planetary Science Division; Science Mission Directorate; Smithsonian Astrophysical Observatory; Max-Planck-Institut für Astronomie; Eötvös Loránd Tudományegyetem; Gordon and Betty Moore Foundation; National Central University; Space Telescope Science Institute; Queen's University; Johns Hopkins University; Queen's University Belfast; National Aeronautics and Space Administration; Durham University; Smithsonian Institution; National Science Foundation","keywords":"Telescope; Observatory; Throughput; Term (time); Remote sensing; Computer science; Aperture (computer memory); Physics; Real-time computing; Environmental science; Optics; Geography; Astronomy; Telecommunications","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":[0.002318079,0.0005735568,0.000388536,0.003209166,0.0006955008,0.001406447,0.0007624207,0.0005100282,0.001521724],"category_scores_gemma":[0.006295454,0.0003429448,0.0002738851,0.002926089,0.0005982287,0.001441167,0.001153373,0.0008747018,0.0009285175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002608667,"about_ca_system_score_gemma":0.000741485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09643638,"about_ca_topic_score_gemma":0.1011892,"domain_scores_codex":[0.9985317,0.0000957471,0.00007078008,0.000421756,0.0006121976,0.0002677096],"domain_scores_gemma":[0.9941094,0.0007142963,0.001305261,0.0009941485,0.002040282,0.0008365568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005130224,0.0002757444,0.8793542,0.0001548144,0.0003108383,0.0003655163,0.001445361,0.01518205,0.01162881,0.0009315929,0.02835605,0.06148202],"study_design_scores_gemma":[0.00001361235,0.00005739636,0.9814385,0.00002335045,0.00001884374,0.00008874678,0.0002209943,0.008877792,0.001289608,0.0001404378,0.007801923,0.00002878665],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733599,0.0003122225,0.002093664,0.0002319159,0.00002533268,0.000040701,0.01815824,0.0005853308,0.005192692],"genre_scores_gemma":[0.9517544,0.0001799321,0.002482517,0.00006523239,0.00004657891,0.00005202883,0.04338639,0.0001708941,0.001861926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09643638,"threshold_uncertainty_score":0.1917501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03830662825196262,"score_gpt":0.2868721713701248,"score_spread":0.2485655431181621,"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."}}