{"id":"W4393341832","doi":"10.1515/jnma-2024-0025","title":"Optimal evaluation of symmetry-adapted <i>n</i>-correlations via recursive contraction of sparse symmetric tensors","year":2024,"lang":"en","type":"article","venue":"Journal of Numerical Mathematics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Contraction (grammar); Symmetry (geometry); Mathematics; Physics; Pure mathematics; Mathematical physics; Combinatorics; Geometry; Philosophy; Linguistics","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":[0.001992779,0.0006463345,0.0008987076,0.0005225769,0.0005734002,0.0009366178,0.00146557,0.0007986383,0.003332913],"category_scores_gemma":[0.008458391,0.0003327645,0.0005520765,0.0005191728,0.001246333,0.001470086,0.001631898,0.001093646,0.0006705591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119521,"about_ca_system_score_gemma":0.002263976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003030125,"about_ca_topic_score_gemma":0.004562349,"domain_scores_codex":[0.9988342,0.0004071173,0.00005077916,0.0001008513,0.0003921792,0.0002148556],"domain_scores_gemma":[0.9976549,0.001075497,0.0001676925,0.0003945831,0.0005116761,0.0001956352],"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":[0.0006185811,0.0002221002,0.002667529,0.0002401879,0.00007478434,0.0003319574,0.000243983,0.6360348,0.02669789,0.1690813,0.00484561,0.1589412],"study_design_scores_gemma":[0.00001069751,0.00002811301,0.00007689968,0.000005354073,0.000003145889,0.00001640716,0.00001334749,0.9867452,0.002577967,0.01019313,0.0003245846,0.000005269523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1087988,0.0001523382,0.8834956,0.0002992078,0.0000590685,0.0000937621,0.00006758494,0.0009960324,0.006037547],"genre_scores_gemma":[0.6690323,0.00006306452,0.3282209,0.000126102,0.00002741775,0.00009000349,0.0001375997,0.0003849168,0.00191755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003332913,"threshold_uncertainty_score":0.0111497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03201788785089341,"score_gpt":0.3170195512254993,"score_spread":0.2850016633746059,"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."}}