{"id":"W4396945264","doi":"10.48550/arxiv.2405.08106","title":"Tensor networks for $p$-spin models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada First Research Excellence Fund","keywords":"Tensor (intrinsic definition); Spin (aerodynamics); Physics; Theoretical physics; Computer science; Statistical physics; Mathematics; Pure mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001191813,0.000918041,0.0008071958,0.0008705088,0.001065581,0.001670911,0.001855054,0.001144793,0.007298413],"category_scores_gemma":[0.0075095,0.0004995841,0.001098098,0.0009578682,0.001338039,0.004027406,0.002134807,0.002395849,0.001250188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001684427,"about_ca_system_score_gemma":0.001380824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002782076,"about_ca_topic_score_gemma":0.005243637,"domain_scores_codex":[0.9992767,0.0002338305,0.00003962418,0.00012464,0.0002222547,0.0001029193],"domain_scores_gemma":[0.9981025,0.0007939543,0.0002074827,0.0005131197,0.0002329158,0.0001499956],"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":[0.00008958148,0.00007104925,0.0004893829,0.0001692565,0.00003514819,0.0001094024,0.0001277464,0.3731191,0.002744602,0.5755919,0.006373259,0.04107977],"study_design_scores_gemma":[0.000005293342,0.000009231516,0.00003617906,0.000007131365,0.000003551743,0.00001727244,0.00001222546,0.7615763,0.0004125376,0.2364322,0.001482765,0.000005304055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03336107,0.0002169362,0.9549633,0.0007479029,0.00007408667,0.00007650203,0.0002902063,0.0009413443,0.009328717],"genre_scores_gemma":[0.4546984,0.000596015,0.5309938,0.0003503833,0.0001845285,0.0004397894,0.00137841,0.0009447889,0.01041388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007298413,"threshold_uncertainty_score":0.02441561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08867957588949821,"score_gpt":0.2110619033248309,"score_spread":0.1223823274353327,"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."}}