{"id":"W4376123207","doi":"10.48550/arxiv.2305.05622","title":"Multilinear Hyperquiver Representations","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Division of Materials Research; Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Multilinear map; Quiver; Hypergraph; Tensor (intrinsic definition); Mathematics; Representation (politics); Dimension (graph theory); Eigenvalues and eigenvectors; Tuple; Linear subspace; Pure mathematics; Variety (cybernetics); Algebra over a field; Discrete 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001212694,0.0002255496,0.0002682519,0.0002032303,0.0001926107,0.00004585259,0.0005007684,0.0002345996,0.0002652223],"category_scores_gemma":[0.0001050315,0.0002709185,0.0002557814,0.0003847163,0.0001055195,0.00007913465,0.0005942272,0.0004487274,0.0009348352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001021475,"about_ca_system_score_gemma":0.00006273674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001093696,"about_ca_topic_score_gemma":0.00006500467,"domain_scores_codex":[0.9986507,0.00008109442,0.0002320221,0.0007322194,0.00007750692,0.0002264885],"domain_scores_gemma":[0.9980689,0.0003514342,0.0001996896,0.001071961,0.0001724624,0.0001356022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003652152,0.0004227377,0.002425409,0.0001578523,0.0002790679,0.0002302299,0.000459914,0.05341469,0.0002415374,0.8978075,0.04440981,0.0001146701],"study_design_scores_gemma":[0.0007316986,0.00001512447,0.00289577,0.00009216723,0.0003056395,0.000005669418,0.0005685478,0.1515649,0.0002409998,0.8389665,0.004026027,0.0005869578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8154914,0.00001313564,0.1692649,0.001016082,0.0005733614,0.001013546,0.0003961627,0.001751172,0.01048022],"genre_scores_gemma":[0.9658924,0.0000733318,0.004295585,0.0000748133,0.0001036367,0.000007120518,0.000135099,0.00005609364,0.02936186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1649693,"threshold_uncertainty_score":0.9999743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2862234368889384,"score_gpt":0.2895679401618224,"score_spread":0.003344503272884047,"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."}}