{"id":"W4405419030","doi":"10.48550/arxiv.2408.11808","title":"Distance Correlation in Multiple Biased Sampling Models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Correlation; Distance sampling; Sampling (signal processing); Statistics; Mathematics; Statistical physics; Computer science; Physics; Geometry; Biology; Computer vision; Abundance (ecology)","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"],"consensus_categories":[],"category_scores_codex":[0.000587519,0.0002694279,0.0003400595,0.0003779808,0.00007448375,0.00006337537,0.0003145599,0.0003550136,0.00003174342],"category_scores_gemma":[0.000387877,0.0003203794,0.0001518131,0.0004422714,0.00005662329,0.0001239448,0.0004213322,0.0007737387,0.00004100152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003413637,"about_ca_system_score_gemma":0.00009756913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004110523,"about_ca_topic_score_gemma":0.0003688652,"domain_scores_codex":[0.998567,0.0001165803,0.0003158921,0.000681328,0.00008305128,0.000236162],"domain_scores_gemma":[0.9981944,0.0008616566,0.0001969385,0.0005787844,0.0001012084,0.00006703883],"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":[0.00005450399,0.00008849814,0.0033708,0.0004447141,0.0000363211,0.00005032579,0.000534011,0.8144304,0.00001067988,0.1803399,0.0002859939,0.000353947],"study_design_scores_gemma":[0.0001230587,0.000005094933,0.0001233923,0.00041094,0.00002364662,4.83644e-7,0.00005697766,0.5087103,0.0000286853,0.4903223,0.00001884125,0.0001763457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3369371,0.00005374001,0.660791,0.00001445896,0.0002529431,0.0002741352,0.00004429357,0.0005323719,0.001099929],"genre_scores_gemma":[0.9864244,0.00006262677,0.01259838,0.0000118126,0.00003830262,0.000004002864,0.0000810703,0.00004669901,0.000732713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6494873,"threshold_uncertainty_score":0.9999248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3945868581052651,"score_gpt":0.2763573893743377,"score_spread":0.1182294687309274,"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."}}