{"id":"W83471987","doi":"10.1007/10849171_26","title":"Estimation of Correlations in Large Samples","year":2006,"lang":"en","type":"book-chapter","venue":"ESO astrophysics symposia","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"","keywords":"Cosmic microwave background; Estimator; Random field; Galaxy; Statistical physics; Algorithm; Cluster analysis; Lossless compression; Computer science; Physics; Astrophysics; Mathematics; Artificial intelligence; Statistics; Data compression","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":[],"consensus_categories":[],"category_scores_codex":[0.000085129,0.0002365207,0.0003355742,0.0001662188,0.00008950288,0.00007334808,0.0001998685,0.00007301331,0.0005525533],"category_scores_gemma":[0.000003367386,0.0002428251,0.0001873836,0.00008359278,0.0001128078,0.000195391,0.00008855954,0.0002611394,0.0001187095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003994956,"about_ca_system_score_gemma":0.00014508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002146273,"about_ca_topic_score_gemma":0.00001440197,"domain_scores_codex":[0.9986299,0.0000156877,0.0003895602,0.0003069183,0.0003770666,0.0002808645],"domain_scores_gemma":[0.999185,0.00006176608,0.0002546067,0.0003646023,0.00007343791,0.00006064086],"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":[0.00001341656,0.0001083786,0.001377409,0.00002141178,0.00003991479,0.000001566264,0.00008773455,0.009870056,0.0002261045,0.9789711,0.002928148,0.006354745],"study_design_scores_gemma":[0.003046597,0.0003681302,0.0121344,0.001118998,0.0002704649,0.000001317293,0.0003341251,0.03124314,0.003795385,0.8684465,0.07739687,0.001844071],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01081925,0.0001456103,0.1152304,0.00003294095,0.0002670235,0.0004488605,0.002308458,0.00002826497,0.8707193],"genre_scores_gemma":[0.8823966,0.000002380716,0.001320624,0.000002493023,0.0002430119,0.00001405705,0.00260804,0.00004314668,0.1133697],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8715773,"threshold_uncertainty_score":0.9902122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138470224577626,"score_gpt":0.2467768760217779,"score_spread":0.2353921737760017,"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."}}