{"id":"W3123069892","doi":"10.20944/preprints201904.0077.v1","title":"Evaluating Approximations and Heuristic Measures of Integrated Information","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Norges Forskningsråd; European Commission","keywords":"Heuristic; Uniqueness; Approximations of π; State (computer science); Entropy (arrow of time); Stochastic matrix; Binary number; Matrix (chemical analysis); Mathematics; Information theory; Applied mathematics; Computer science; Statistical physics; Mathematical optimization; Markov chain; Algorithm; Physics; Statistics; Mathematical analysis; Quantum mechanics; Chemistry","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":[0.01222058,0.001094332,0.001317308,0.002985392,0.000565474,0.002768832,0.002066419,0.001565406,0.001091004],"category_scores_gemma":[0.1440307,0.0006829603,0.0006427713,0.001570724,0.002820642,0.005633638,0.002672644,0.001775896,0.0001585024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003885019,"about_ca_system_score_gemma":0.002135834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00510126,"about_ca_topic_score_gemma":0.003204447,"domain_scores_codex":[0.9941925,0.002784011,0.0003697568,0.0008079786,0.001547125,0.000298508],"domain_scores_gemma":[0.8794979,0.100212,0.006784579,0.008083508,0.004354914,0.001067095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004108207,0.0001145566,0.01725506,0.0002805942,0.0002371332,0.00009842974,0.0005043785,0.8791328,0.001005818,0.06463919,0.0006883739,0.03563276],"study_design_scores_gemma":[0.00001488377,0.0001217613,0.001631032,0.00004568528,0.00001897989,0.00003879453,0.00009600676,0.9679152,0.0007175996,0.02911484,0.0002578621,0.0000275039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3964595,0.001275148,0.5953543,0.0006992838,0.00008544026,0.000202063,0.0003868952,0.0005701618,0.004967179],"genre_scores_gemma":[0.8808982,0.0001971584,0.1178239,0.00009634021,0.00002826884,0.0001801724,0.0003595968,0.00006562255,0.0003507066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01222058,"threshold_uncertainty_score":0.06462938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2704547047704846,"score_gpt":0.3778631632941068,"score_spread":0.1074084585236222,"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."}}