{"id":"W4403602162","doi":"10.1103/physrevx.14.041018","title":"Capturing Long-Range Memory Structures with Tree-Geometry Process Tensors","year":2024,"lang":"en","type":"article","venue":"Physical Review X","topic":"Quantum many-body systems","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Compute Canada","funders":"Australian Government; Australian Research Council; Alexander von Humboldt-Stiftung","keywords":"Range (aeronautics); Process (computing); Computer science; Geometry; Tree (set theory); Statistical physics; Physics; Mathematics; Mathematical analysis; Aerospace engineering; Engineering","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":[0.0006427786,0.0005110682,0.0004773017,0.0006244,0.0005890126,0.001196117,0.001065655,0.0008540197,0.00219285],"category_scores_gemma":[0.002400191,0.0002742473,0.0005851528,0.0004700856,0.001436353,0.00305923,0.001044131,0.001018838,0.0002200971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009429155,"about_ca_system_score_gemma":0.0009270298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002164606,"about_ca_topic_score_gemma":0.002068673,"domain_scores_codex":[0.9998319,0.00005610323,0.000006924889,0.00002913791,0.00004162778,0.00003429692],"domain_scores_gemma":[0.9991407,0.0003403662,0.0001765285,0.0001731397,0.00007284259,0.00009637696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002544142,0.00003356551,0.0007107021,0.00002881813,0.00001306189,0.0001277463,0.0001283717,0.181372,0.005946273,0.8074418,0.000357715,0.003814521],"study_design_scores_gemma":[0.000003260726,0.00001128706,0.0001104515,0.000002803749,0.000002762601,0.00002492152,0.00001500941,0.8478441,0.0005496167,0.1508923,0.0005361488,0.000007427265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1564682,0.0002281443,0.834942,0.0003651874,0.00006280811,0.00005129828,0.0001157721,0.0002361918,0.007530313],"genre_scores_gemma":[0.9127102,0.0003020675,0.08359464,0.00007702878,0.00005430864,0.00007125187,0.00009205653,0.00008576407,0.003012762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00219285,"threshold_uncertainty_score":0.007335842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157478734934231,"score_gpt":0.2993269963118046,"score_spread":0.2877522089624623,"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."}}