{"id":"W3197943580","doi":"10.48550/arxiv.2002.11259","title":"Dimensional Analysis in Statistical Modelling","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Logarithm; Statistical model; Computer science; Scale (ratio); Statistical theory; Frequentist probability; Natural (archaeology); Statistical physics; Bayesian probability; Mathematics; Artificial intelligence; Statistics; Physics","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":[0.0001232198,0.000241452,0.0004160633,0.0004006574,0.00006516901,0.0001330101,0.001334146,0.0001820369,0.00005522766],"category_scores_gemma":[0.00001830614,0.000271524,0.0001639846,0.001727035,0.00006275401,0.0002345369,0.001520641,0.0005955947,0.00006383085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001061054,"about_ca_system_score_gemma":0.0002672121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002169041,"about_ca_topic_score_gemma":0.00005946608,"domain_scores_codex":[0.9980792,0.00008258289,0.0002269175,0.001189654,0.0001183088,0.0003033708],"domain_scores_gemma":[0.998834,0.0001054363,0.0001404108,0.0006367541,0.00008303185,0.0002003739],"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":[0.000008161225,0.00003248137,0.001953193,0.00003017022,0.00008335593,0.0004343073,0.0000659721,0.6449995,0.000001212559,0.3522542,0.00002948772,0.0001079689],"study_design_scores_gemma":[0.0001210906,0.00001518825,0.001517668,0.00002509103,0.0001040907,8.049992e-7,0.000007421778,0.7881892,0.000005331499,0.2097584,0.00002012556,0.0002355522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02576284,0.00002668202,0.9728239,0.000255593,0.0001064889,0.00009360084,0.00002597745,0.00009859981,0.0008062909],"genre_scores_gemma":[0.9669207,0.00003534595,0.03277216,0.0001131058,0.00002247429,4.774474e-7,0.0000275788,0.000007559293,0.0001005521],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9411579,"threshold_uncertainty_score":0.9999737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08345103208852576,"score_gpt":0.1950377063800958,"score_spread":0.1115866742915701,"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."}}