{"id":"W2550850854","doi":"10.22323/1.282.0118","title":"Global fits of scalar singlet dark matter with GAMBIT","year":2017,"lang":"en","type":"preprint","venue":"Proceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016)","topic":"Dark Matter and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gambit; Dark matter; Scalar (mathematics); Physics; Singlet state; Computer science; Particle physics; Mathematics; Quantum mechanics; Computational fluid dynamics; Mechanics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003359705,0.001365494,0.001167966,0.001781258,0.0008392411,0.001783809,0.001683898,0.0009102903,0.007967646],"category_scores_gemma":[0.01245542,0.0006224936,0.001522035,0.00167456,0.0006336675,0.001897819,0.002980341,0.001832922,0.001501682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007453951,"about_ca_system_score_gemma":0.0007209476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006005178,"about_ca_topic_score_gemma":0.004776886,"domain_scores_codex":[0.9992309,0.0004072103,0.00002127481,0.000138608,0.0001004963,0.0001014996],"domain_scores_gemma":[0.9971867,0.001540192,0.00017915,0.0005917436,0.0002941507,0.0002081094],"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":[0.001507416,0.0001823331,0.04643533,0.0002351197,0.0006618293,0.0005566909,0.0005351155,0.7726966,0.003037233,0.1063102,0.02217649,0.0456656],"study_design_scores_gemma":[0.00007307024,0.00003126056,0.002603572,0.00002663738,0.00002622886,0.00007436993,0.00009624138,0.93646,0.0009973741,0.0564473,0.003126029,0.00003799506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4976669,0.0008142795,0.4551031,0.001030314,0.0001364957,0.0001124766,0.00857065,0.01587624,0.02068963],"genre_scores_gemma":[0.9032844,0.0001656638,0.08263396,0.0002764197,0.00004183247,0.000133822,0.007971289,0.00280861,0.002684057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007967646,"threshold_uncertainty_score":0.02665442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02060066252887759,"score_gpt":0.2566822037342916,"score_spread":0.236081541205414,"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."}}