{"id":"W4402665424","doi":"10.36227/techrxiv.172684273.35274476/v1","title":"Enhancing Convergent Cross Mapping: Simple Preprocessing for Noise-Resilient Causal Discovery","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; McGill University","funders":"","keywords":"Preprocessor; Simple (philosophy); Noise (video); Computer science; Artificial intelligence; Algorithm; Epistemology","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":[0.007513087,0.001370989,0.001207852,0.00232621,0.001094494,0.001918531,0.001530346,0.001507729,0.004050971],"category_scores_gemma":[0.04312729,0.0005483284,0.001307271,0.001913993,0.001083207,0.002732494,0.003246348,0.002106793,0.001190637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004879757,"about_ca_system_score_gemma":0.002354007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002777298,"about_ca_topic_score_gemma":0.004004188,"domain_scores_codex":[0.9977877,0.0009324163,0.0001876552,0.0005513597,0.0003962142,0.0001445395],"domain_scores_gemma":[0.9806403,0.01354398,0.0008356453,0.002918574,0.001718899,0.0003425713],"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":[0.0009279268,0.0003832609,0.01382489,0.0007376368,0.0004644477,0.0008751655,0.001108555,0.2088301,0.02410857,0.09015445,0.007098284,0.6514868],"study_design_scores_gemma":[0.00005801645,0.0001397227,0.002833118,0.00006006985,0.00008209941,0.0002521692,0.0001507048,0.8791065,0.01087865,0.1017455,0.004643464,0.00005005615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01766264,0.0002773108,0.9797441,0.0002237774,0.00005357496,0.00005036387,0.0001411524,0.001088059,0.0007589149],"genre_scores_gemma":[0.3936736,0.0004927914,0.6012797,0.0003852197,0.0002137679,0.0002880423,0.001188774,0.0005257975,0.001952313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007513087,"threshold_uncertainty_score":0.03973347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04210191644735579,"score_gpt":0.3223643965823912,"score_spread":0.2802624801350355,"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."}}