{"id":"W6996016301","doi":"","title":"Quantifying the difference between black boxes and their automata approximations","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Black box; Metric (unit); Automaton; Artificial neural network; Upper and lower bounds; Obstacle; Line (geometry); Approximations of π","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":[0.006738588,0.00100223,0.001268101,0.001617035,0.0009425692,0.003032345,0.001411106,0.002239191,0.00287948],"category_scores_gemma":[0.05706383,0.000986721,0.0008318876,0.0009639422,0.003532152,0.007890491,0.003494793,0.003370395,0.000617901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001747255,"about_ca_system_score_gemma":0.001073376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286361,"about_ca_topic_score_gemma":0.001186392,"domain_scores_codex":[0.9935064,0.002416691,0.0004428456,0.001466864,0.001793505,0.0003737576],"domain_scores_gemma":[0.9382581,0.04942344,0.002813494,0.006554564,0.002024536,0.0009259054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008651498,0.0001423493,0.010826,0.0004420022,0.0002039941,0.0001816501,0.0009478494,0.539708,0.01037448,0.3452909,0.001653079,0.08936458],"study_design_scores_gemma":[0.00001344424,0.000257464,0.001222236,0.00007895402,0.00003171881,0.00009481188,0.00009033219,0.8144663,0.003552534,0.1783743,0.001781668,0.00003614882],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1859253,0.00130218,0.8056015,0.0007382412,0.0001526042,0.00007925022,0.0002329307,0.0008939421,0.005074117],"genre_scores_gemma":[0.7681921,0.0005958403,0.2281372,0.0003076848,0.00008683521,0.0002191084,0.0005004652,0.0003997739,0.001560918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006738588,"threshold_uncertainty_score":0.0356375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098488904567911,"score_gpt":0.2674910395503916,"score_spread":0.2365061505047124,"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."}}