{"id":"W2972671918","doi":"10.1093/mnras/staa682","title":"Neural physical engines for inferring the halo mass distribution function","year":2020,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Agence Nationale de la Recherche","keywords":"Dark matter; Physics; Halo; Galaxy; Astrophysics; Dark matter halo; Halo effect; Cosmology; Bayesian probability; Artificial neural network; Dark energy; Statistical physics; Artificial intelligence; Computer science","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":[0.001411715,0.0004025387,0.0003999607,0.0007202959,0.0002716525,0.0007902556,0.001100851,0.0008227869,0.001839365],"category_scores_gemma":[0.006810418,0.0004251974,0.0004705054,0.0003829988,0.0005895439,0.001453136,0.0007962829,0.0009884471,0.0002707013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165582,"about_ca_system_score_gemma":0.0007505217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01235842,"about_ca_topic_score_gemma":0.01342467,"domain_scores_codex":[0.9998316,0.00006237476,0.000008466967,0.00003510246,0.00003777146,0.00002473166],"domain_scores_gemma":[0.9985251,0.0009345543,0.0001393495,0.0001270466,0.0001989001,0.00007502393],"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":[0.00008136604,0.00003248249,0.006106312,0.00002952233,0.00005071954,0.00002922868,0.00003681159,0.9527944,0.001872239,0.01033552,0.0003426102,0.02828868],"study_design_scores_gemma":[0.000002959933,0.000003166853,0.0002853754,0.000001825753,0.000001835092,0.000002257561,0.000001565266,0.9957832,0.0002750893,0.003595419,0.00004525982,0.000002140623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3042972,0.0002401395,0.6912063,0.0005301535,0.0000350165,0.00004266015,0.0003218215,0.001182397,0.002144207],"genre_scores_gemma":[0.9137533,0.00008849509,0.08429921,0.000110473,0.00002361187,0.00003956975,0.0002655885,0.00007978139,0.001339923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01235842,"threshold_uncertainty_score":0.02457297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008548845080074075,"score_gpt":0.1932974962417167,"score_spread":0.1847486511616426,"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."}}