{"id":"W4381573116","doi":"10.48550/arxiv.2306.11188","title":"Invariant correlation under marginal transforms","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Invariant (physics); Correlation; Mathematics; Pure mathematics; Geometry; Mathematical physics","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":[0.001599084,0.0003115363,0.0004241712,0.0005367831,0.0001676108,0.0001695317,0.001304905,0.0004509082,0.0003208716],"category_scores_gemma":[0.0005040177,0.0002730879,0.0002826051,0.001078388,0.0001475568,0.0002440805,0.0004709033,0.0006875328,0.001562239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002303406,"about_ca_system_score_gemma":0.0002865728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001074095,"about_ca_topic_score_gemma":0.00005457537,"domain_scores_codex":[0.9975488,0.0001557801,0.0004363426,0.001134298,0.0003634725,0.0003613596],"domain_scores_gemma":[0.9974595,0.0008517887,0.0002303538,0.001017841,0.0002432828,0.0001972261],"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":[0.00003759886,0.00002603596,0.0008175926,0.00001684807,0.0000413594,0.0001246222,0.00009801782,0.8413599,0.000005727702,0.155495,0.001817446,0.0001598059],"study_design_scores_gemma":[0.0002755957,0.00002533153,0.005945913,0.00005284636,0.00006315135,0.000003760839,0.0002244895,0.5718054,0.000004730382,0.4208412,0.0004628829,0.0002947528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03516163,0.00002682325,0.9595166,0.0002670776,0.001600458,0.000320339,0.0000463456,0.0002999063,0.002760858],"genre_scores_gemma":[0.9785381,0.00006593019,0.0005960827,0.00004034345,0.0001044478,0.000001317501,0.00003408706,0.00003224537,0.02058748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9589205,"threshold_uncertainty_score":0.9999721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3027216274371801,"score_gpt":0.2483350582241737,"score_spread":0.05438656921300633,"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."}}