{"id":"W4403303551","doi":"10.1037/met0000691","title":"Lagged multidimensional recurrence quantification analysis for determining leader–follower relationships within multidimensional time series.","year":2024,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Deutsche Forschungsgemeinschaft","keywords":"Series (stratigraphy); Recurrence quantification analysis; Multidimensional analysis; Statistics; Statistical analysis; Econometrics; Time series; Psychology; Mathematics; Geology; Nonlinear system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007153639,0.001020512,0.0006939936,0.003832795,0.0005405871,0.001582323,0.0009911687,0.0005767934,0.007364688],"category_scores_gemma":[0.04270435,0.0003584737,0.001460733,0.004180809,0.0006303263,0.002134113,0.001362946,0.001540724,0.00155725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006019191,"about_ca_system_score_gemma":0.001116878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002867884,"about_ca_topic_score_gemma":0.002622964,"domain_scores_codex":[0.9971039,0.001470989,0.0002795326,0.0005852827,0.0004690761,0.00009125938],"domain_scores_gemma":[0.9836363,0.01106865,0.001841765,0.00185409,0.001343404,0.0002557221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000398816,0.0001849821,0.05128548,0.002301343,0.001475382,0.0004565785,0.00230356,0.0479187,0.01577095,0.1153464,0.02660092,0.735957],"study_design_scores_gemma":[0.00006350914,0.000390902,0.06994163,0.0005807825,0.0004656627,0.0007764894,0.0009256142,0.7525339,0.00725891,0.1262581,0.04045787,0.0003465778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01027573,0.0008330393,0.9851376,0.0001410728,0.0001245992,0.0001755958,0.001289994,0.0007998962,0.001222437],"genre_scores_gemma":[0.2557045,0.0007620761,0.7368035,0.000124894,0.0002126743,0.001320681,0.003091261,0.0004666548,0.001513763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007364688,"threshold_uncertainty_score":0.03783256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1422475882131395,"score_gpt":0.427926050321445,"score_spread":0.2856784621083055,"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."}}