{"id":"W2208485803","doi":"10.1088/1538-3873/128/970/124401","title":"Campaign 9 of the<i>K2</i>Mission: Observational Parameters, Scientific Drivers, and Community Involvement for a Simultaneous Space- and Ground-based Microlensing Survey","year":2016,"lang":"en","type":"article","venue":"Publications of the Astronomical Society of the Pacific","topic":"Stellar, planetary, and galactic studies","field":"Physics and Astronomy","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Science and Technology Facilities Council; Jet Propulsion Laboratory; Regione Campania; Narodowym Centrum Nauki; Korea Astronomy and Space Science Institute; Chinese Academy of Sciences; Universities Space Research Association; National Natural Science Foundation of China; California Institute of Technology; National Aeronautics and Space Administration","keywords":"Gravitational microlensing; Parallax; Planet; Leverage (statistics); Bulge; Anticipation (artificial intelligence)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007472994,0.001117198,0.0006261125,0.001858872,0.002455764,0.003601128,0.00130806,0.001769027,0.006221139],"category_scores_gemma":[0.005658867,0.0005985812,0.0007389474,0.001965191,0.0006816994,0.001925513,0.003885198,0.002540153,0.003278411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004594858,"about_ca_system_score_gemma":0.006346222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07790583,"about_ca_topic_score_gemma":0.2094541,"domain_scores_codex":[0.9963456,0.0003597045,0.0001126124,0.000563709,0.001867719,0.0007507391],"domain_scores_gemma":[0.9902633,0.0006275124,0.0017859,0.0009492217,0.002628562,0.003745417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00250541,0.0005091802,0.2465677,0.0005037756,0.000317635,0.0003655842,0.001699477,0.000823744,0.02095824,0.001951558,0.6733611,0.05043662],"study_design_scores_gemma":[0.0003815749,0.000275436,0.7378412,0.0001260142,0.0001650566,0.0001229928,0.001190011,0.001346145,0.004701544,0.0008902733,0.2527988,0.0001610321],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3323895,0.002124675,0.008896363,0.02681895,0.003648517,0.002534648,0.4984009,0.004341548,0.1208449],"genre_scores_gemma":[0.3947548,0.000904549,0.03173129,0.007027192,0.001911275,0.002570233,0.5114781,0.003920787,0.04570171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07790583,"threshold_uncertainty_score":0.1549047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0492069440821313,"score_gpt":0.2424984524369765,"score_spread":0.1932915083548452,"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."}}