{"id":"W2950135190","doi":"10.48550/arxiv.1307.8161","title":"Mutually Unbiased Weighing Matrices","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"graph theory and CDMA systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Lethbridge","funders":"","keywords":"Hadamard transform; Mutually unbiased bases; Mathematics; Upper and lower bounds; Best linear unbiased prediction; Computer science; Algorithm; Combinatorics; Statistics; Pure mathematics; Artificial intelligence; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001733173,0.0003788555,0.0003759603,0.0002921509,0.0001001604,0.0001011566,0.000717895,0.0003976009,0.0002806007],"category_scores_gemma":[0.00001018121,0.0004440582,0.0002410251,0.0003489354,0.00006217821,0.000227705,0.0002804898,0.000615891,0.0006386262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001321645,"about_ca_system_score_gemma":0.00003140561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001297599,"about_ca_topic_score_gemma":0.00002170034,"domain_scores_codex":[0.9987169,0.00009646031,0.000222845,0.0005242507,0.00006797468,0.0003715869],"domain_scores_gemma":[0.9988695,0.00008667224,0.0001070229,0.0007048372,0.00006590465,0.0001660296],"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":[0.00001983954,0.00003783354,0.001198898,0.0007272963,0.000384193,0.0002692701,0.0004511964,0.8980546,0.0004323341,0.09568388,0.002385998,0.0003546731],"study_design_scores_gemma":[0.001173494,0.00006215984,0.001354534,0.0008427601,0.0004557707,0.0000131084,0.001247138,0.8725727,0.001017435,0.109186,0.009648572,0.00242636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9630783,0.0003640987,0.01371402,0.00000643882,0.001709906,0.0003258175,0.00002912664,0.0009162105,0.01985609],"genre_scores_gemma":[0.9978819,0.0002634753,0.00007611499,0.00001693676,0.0001729014,0.000002294356,0.00002840806,0.00005844318,0.001499461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03480367,"threshold_uncertainty_score":0.9998011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03665512459433039,"score_gpt":0.1441904254200439,"score_spread":0.1075353008257135,"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."}}