{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068863,0.0009741301,0.0007690862,0.004353488,0.001222928,0.002908753,0.001124183,0.00113281,0.007348737],"category_scores_gemma":[0.004244768,0.0003638167,0.0007557361,0.001766771,0.002769625,0.005596622,0.001826276,0.001803622,0.001100848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062254,"about_ca_system_score_gemma":0.0004238098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007690331,"about_ca_topic_score_gemma":0.0008607543,"domain_scores_codex":[0.9989237,0.0002348721,0.00005578978,0.0002875985,0.0002855645,0.0002123512],"domain_scores_gemma":[0.9978191,0.0005244227,0.0004277932,0.0003676281,0.0004897783,0.0003713267],"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":[0.00002563314,0.00002144004,0.0008215621,0.00005809068,0.00001187296,0.0001049207,0.000221542,0.003042273,0.002332211,0.982366,0.001534137,0.009460352],"study_design_scores_gemma":[0.000003373539,0.0000252141,0.0004317481,0.0000283891,0.000008254108,0.0001477577,0.0001342402,0.02937396,0.001608947,0.9636941,0.004523133,0.00002110097],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3961032,0.001190481,0.5521032,0.00134868,0.0004428611,0.0001031233,0.0006673077,0.0006409662,0.04740021],"genre_scores_gemma":[0.9085917,0.0007189268,0.07094397,0.0003157577,0.0004051014,0.0001086839,0.0006176929,0.0004348804,0.01786319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007348737,"threshold_uncertainty_score":0.024584,"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."}}