{"id":"W2414267504","doi":"10.1103/physrevlett.117.222501","title":"How Many-Body Correlations and<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mi>α</mml:mi></mml:math>Clustering Shape<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mrow><mml:mmultiscripts><mml:mrow><mml:mi>He</mml:mi></mml:mrow><mml:mprescripts/><mml:none/><mml:mrow><mml:mn>6</mml:mn></mml:mrow></mml:mmultiscripts></mml:mrow></mml:math>","year":2016,"lang":"lv","type":"article","venue":"Physical Review Letters","topic":"Nuclear physics research studies","field":"Physics and Astronomy","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"TRIUMF","funders":"Oak Ridge National Laboratory; National Research Council Canada; Lawrence Livermore National Laboratory; Nuclear Physics; Natural Sciences and Engineering Research Council of Canada; TRIUMF; U.S. Department of Energy","keywords":"Physics; Cluster analysis; Halo; Algorithm; Mathematical physics; Statistical physics; Computer science; Quantum mechanics; Machine learning","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":[0.0008685456,0.0005224424,0.0007145542,0.0006473301,0.001201605,0.003834505,0.001149431,0.00137357,0.0164574],"category_scores_gemma":[0.0056343,0.0004554405,0.000801771,0.0009384857,0.001278006,0.006185634,0.0006801692,0.00157617,0.004064739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000698087,"about_ca_system_score_gemma":0.0011159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005352153,"about_ca_topic_score_gemma":0.007147112,"domain_scores_codex":[0.9995663,0.0001574953,0.00001423039,0.0001136015,0.00008512308,0.00006336601],"domain_scores_gemma":[0.9983944,0.0006474937,0.0002016939,0.0003946821,0.0002114048,0.0001502676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007141482,0.00009678787,0.005582711,0.0002867946,0.00007251972,0.000213733,0.0003075759,0.04010912,0.002755407,0.8908375,0.02363957,0.03602683],"study_design_scores_gemma":[0.00001947566,0.00002368914,0.003562683,0.00009295038,0.00003731982,0.0001752314,0.0002316565,0.2857181,0.003490564,0.6944595,0.01208113,0.0001077076],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3167235,0.006221273,0.4351595,0.01832283,0.001817671,0.0001094886,0.005019696,0.003420565,0.2132054],"genre_scores_gemma":[0.9337637,0.003508283,0.0356729,0.001005394,0.0004112574,0.00009753669,0.002000751,0.001033036,0.02250709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0164574,"threshold_uncertainty_score":0.0550555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02342897972105623,"score_gpt":0.2637032768193384,"score_spread":0.2402742970982822,"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."}}