{"id":"W4385198156","doi":"10.1103/physreve.108.014126","title":"Cellular automata can classify data by inducing trajectory phase coexistence","year":2023,"lang":"en","type":"article","venue":"Physical review. E","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Ottawa","funders":"Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; Office of Science; U.S. Department of Energy","keywords":"Cellular automaton; Automaton; Computer science; Trajectory; Binary number; Basis (linear algebra); Statistical physics; Population; Stochastic cellular automaton; Algorithm; Nonlinear system; Monte Carlo method; Theoretical computer science; Artificial intelligence; Mathematics; Physics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007770894,0.000341331,0.0005233326,0.001335033,0.0007829249,0.001600996,0.0005653948,0.0011932,0.001292955],"category_scores_gemma":[0.006048391,0.0002929271,0.0008014441,0.0006695383,0.002191838,0.002292814,0.000984399,0.00105087,0.0002985664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007471755,"about_ca_system_score_gemma":0.0004272753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001527257,"about_ca_topic_score_gemma":0.001105008,"domain_scores_codex":[0.9995888,0.00009856073,0.00003895073,0.0001083,0.0001107927,0.00005457568],"domain_scores_gemma":[0.9967751,0.00194237,0.0004073139,0.0004216807,0.0003234902,0.0001301006],"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":[0.0001394955,0.00007362572,0.007182984,0.0002254179,0.00008375524,0.0003027511,0.0005344122,0.3098521,0.03711872,0.5846546,0.001653647,0.05817851],"study_design_scores_gemma":[0.00002063088,0.00005220491,0.0007489317,0.00002872247,0.00001816021,0.0001256227,0.00007247209,0.686156,0.008412916,0.3010347,0.003299232,0.00003037935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2776815,0.000828132,0.7112079,0.001034317,0.0001202852,0.00009338171,0.0001999931,0.000581441,0.008253],"genre_scores_gemma":[0.9037424,0.0003628439,0.09364904,0.0001912916,0.00005118601,0.0001139724,0.0001937967,0.00006532745,0.001630089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001600996,"threshold_uncertainty_score":0.005421162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07983699079238436,"score_gpt":0.3849885873329732,"score_spread":0.3051515965405888,"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."}}