{"id":"W2772792503","doi":"10.1016/j.physa.2017.12.009","title":"Quantifying the range of cross-correlated fluctuations using a<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"mml87\" display=\"inline\" overflow=\"scroll\" altimg=\"si1.gif\"><mml:mi>q</mml:mi></mml:math>–<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"mml88\" display=\"inline\" overflow=\"scroll\" altimg=\"si2.gif\"><mml:mi>L</mml:mi></mml:math>dependent AHXA coefficient","year":2017,"lang":"en","type":"article","venue":"Physica A Statistical Mechanics and its Applications","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Series (stratigraphy); Correlation coefficient; Range (aeronautics); Mathematics; Cross-correlation; Correlation; Statistics; Statistical physics; Physics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001231011,0.0007756539,0.0008166789,0.0002398245,0.003134867,0.001503238,0.001330635,0.0006689642,0.0001827216],"category_scores_gemma":[0.0007886705,0.0009081203,0.0008441102,0.0006623038,0.0005566276,0.0008228563,0.001170831,0.0009066103,0.001098472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005878223,"about_ca_system_score_gemma":0.0003543108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002215511,"about_ca_topic_score_gemma":0.0005639979,"domain_scores_codex":[0.9942263,0.00009346003,0.002073099,0.00157973,0.0008034463,0.001224004],"domain_scores_gemma":[0.9935387,0.0009909255,0.002421073,0.00227246,0.000267102,0.0005097727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002132496,0.0005697158,0.00001699652,0.000460551,0.0008199259,0.0000427826,0.0006870271,0.00379688,0.001321354,0.9909596,0.0002569954,0.0008548739],"study_design_scores_gemma":[0.001149225,0.0003581231,0.0002786503,0.0002788407,0.0007443342,0.00009343102,0.0005059424,0.9838445,0.0007727907,0.006668456,0.004502271,0.0008034863],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9197854,0.0008856031,0.07214735,0.0004368881,0.0007620282,0.0003354545,0.004360486,0.0001392602,0.001147494],"genre_scores_gemma":[0.9930227,0.0007856156,0.003024449,0.0002936854,0.0006457221,0.0008908749,0.0009466666,0.0002725974,0.0001176753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9842912,"threshold_uncertainty_score":0.9996793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03424162233657672,"score_gpt":0.2741476086243794,"score_spread":0.2399059862878027,"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."}}