{"id":"W2737468198","doi":"","title":"Estimating the binary fraction of central stars of planetary nebulae using the infrared excess method","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Astronomy and Astrophysical Research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Planetary nebula; Physics; Nebula; Astrophysics; Stars; Astronomy; Binary number; Infrared; Emission nebula; Protoplanetary nebula; Infrared excess; Binary star; Population; Asymptotic giant branch; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008115809,0.0003749559,0.0004450358,0.003032946,0.0003672937,0.00103309,0.0004677662,0.0004859128,0.001255345],"category_scores_gemma":[0.002302623,0.0002543268,0.0003170146,0.000749209,0.0002137738,0.0006205137,0.0007058181,0.0002026412,0.0006382527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001958752,"about_ca_system_score_gemma":0.0001959738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001382483,"about_ca_topic_score_gemma":0.001500846,"domain_scores_codex":[0.9996326,0.00008011302,0.0000232202,0.0001297696,0.00007250244,0.00006180916],"domain_scores_gemma":[0.9987822,0.0005230589,0.0002368193,0.0001553429,0.0001803094,0.0001223173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001132692,0.0000987299,0.8074425,0.0001371376,0.0002352591,0.0002254787,0.0001277797,0.01149592,0.06542782,0.001824387,0.0004967409,0.1113554],"study_design_scores_gemma":[0.00003856052,0.0001288767,0.7656518,0.0000406453,0.000325495,0.0009017405,0.0002327073,0.197934,0.02963224,0.003146354,0.001922115,0.00004548916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9439991,0.00118377,0.05246572,0.00003538274,0.00001736815,0.00001036843,0.0003164355,0.0001963487,0.001775502],"genre_scores_gemma":[0.9887915,0.000167492,0.01001879,0.000009765024,0.000017003,0.00000496033,0.0003798174,0.0000219668,0.0005887601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003032946,"threshold_uncertainty_score":0.004292071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247689253165751,"score_gpt":0.2861550678028716,"score_spread":0.2636781752712141,"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."}}