{"id":"W1984667514","doi":"10.1038/srep00315","title":"Revisiting detrended fluctuation analysis","year":2012,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":258,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Detrended fluctuation analysis; Estimator; Econometrics; Hurst exponent; Computer science; Detrended correspondence analysis; Contrast (vision); Series (stratigraphy); Statistics; Mathematics; Statistical physics; Artificial intelligence; Geology; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002739029,0.0001048259,0.0003900233,0.0007059605,0.0003174874,0.0003051778,0.00009042091,0.00004203566,0.003472554],"category_scores_gemma":[0.0001708134,0.000112556,0.000337452,0.002328634,0.00004903412,0.0004120076,0.00005410121,0.00005240077,0.0004378408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006896107,"about_ca_system_score_gemma":0.000009502831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002840203,"about_ca_topic_score_gemma":0.00003125793,"domain_scores_codex":[0.9981513,0.00001519133,0.0008944209,0.0005080088,0.00009073746,0.0003403098],"domain_scores_gemma":[0.9982703,0.00001699237,0.0007390492,0.0007865993,0.00006171899,0.0001253333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001584778,0.00005014246,0.9499248,0.00002287299,0.0006542035,0.00001330183,0.0007823504,0.0003394285,0.0002677646,0.04150598,0.002976344,0.003461224],"study_design_scores_gemma":[0.0001268944,0.000009910586,0.2747354,0.00001500673,0.0004016133,0.00005808637,0.0003285113,0.01091017,0.0004480795,0.0359515,0.6763599,0.0006549667],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8822693,0.005980056,0.02048602,0.0002518949,0.007125747,0.0002201771,0.00001794425,0.0001225188,0.08352629],"genre_scores_gemma":[0.9901147,0.000003421156,0.0007138558,0.00001421987,0.0002356867,0.000008287838,0.00008901517,0.000009182939,0.008811588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6751894,"threshold_uncertainty_score":0.9974384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03289142212162841,"score_gpt":0.232470378680575,"score_spread":0.1995789565589466,"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."}}