{"id":"W7084626210","doi":"","title":"A Numerical Rosenblatt Method for Forced Variable Independence","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Alexander von Humboldt Studies","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Univerzita Karlova v Praze","keywords":"Random variable; Variable (mathematics); Observable; Hidden variable theory; Independence (probability theory); Numerical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.0003728518,0.0001920567,0.0003848527,0.00008208032,0.0003760237,0.00001062678,0.0002760061,0.0002881406,0.0003128361],"category_scores_gemma":[0.0004197433,0.0001724886,0.0001057847,0.0002389476,0.0001126969,0.00008248264,0.0001651311,0.0002965782,0.0002884254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004899067,"about_ca_system_score_gemma":0.0001084669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001713849,"about_ca_topic_score_gemma":0.00001530527,"domain_scores_codex":[0.9986833,0.0001231288,0.0002842552,0.0004290548,0.00003011409,0.0004501985],"domain_scores_gemma":[0.9987696,0.0006731163,0.00008234201,0.0003336528,0.0001238573,0.00001742378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005794825,0.0002746931,0.5840002,0.0001554745,0.001762236,0.000005257698,0.0008818685,0.00007510871,0.3217754,0.03563192,0.05033955,0.004518726],"study_design_scores_gemma":[0.007495465,0.0004924629,0.3198303,0.0001604554,0.0005153765,0.00005390758,0.001035174,0.0002479248,0.2407826,0.01084219,0.41754,0.001004059],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2389588,0.003935637,0.7307131,0.002513995,0.002768475,0.001235012,0.00004769985,0.0004268227,0.01940043],"genre_scores_gemma":[0.9613411,0.00002384297,0.005809334,0.00136639,0.00003791725,0.000193236,0.00002736767,0.00001827807,0.03118257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7249038,"threshold_uncertainty_score":0.7033881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03245380044170355,"score_gpt":0.3210332544382309,"score_spread":0.2885794539965274,"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."}}