{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03681335,0.0009635914,0.002134354,0.002451582,0.001051441,0.002204553,0.003272573,0.002201361,0.003657569],"category_scores_gemma":[0.131878,0.0008185117,0.001451857,0.003281119,0.004207399,0.0041496,0.003484979,0.00229934,0.0006114857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001727379,"about_ca_system_score_gemma":0.001684692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00333284,"about_ca_topic_score_gemma":0.002424905,"domain_scores_codex":[0.9746348,0.01960119,0.0005828002,0.001890337,0.002588623,0.0007022021],"domain_scores_gemma":[0.8621425,0.1161391,0.008362974,0.008202625,0.004226596,0.0009261533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001249165,0.00005916015,0.00987519,0.0002322134,0.0002080863,0.0004358534,0.0004446525,0.1208983,0.0004014703,0.8206528,0.001811278,0.04485615],"study_design_scores_gemma":[0.00005822053,0.00007837097,0.001941226,0.00008790367,0.0000649834,0.0002204139,0.00009251942,0.5742143,0.0003895648,0.4204617,0.002354159,0.00003667964],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03069771,0.000774093,0.9646778,0.0006853776,0.00005135594,0.0001335875,0.0001438235,0.0001142201,0.002722117],"genre_scores_gemma":[0.7469261,0.001963468,0.2414799,0.000700384,0.0003500563,0.00112686,0.0006415676,0.0001344704,0.006677224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03681335,"threshold_uncertainty_score":0.1946899,"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."}}