{"id":"W3084274250","doi":"10.3233/aic-200630","title":"Empirical scaling analyzer: An automated system for empirical analysis of performance scaling","year":2020,"lang":"en","type":"article","venue":"AI Communications","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scaling; Function (biology); Empirical research; Algorithm; Key (lock); Spectrum analyzer; Data mining; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000612394,0.0001240768,0.0003375416,0.0002623897,0.0004161144,0.0001277512,0.002457904,0.00007804103,0.000003376242],"category_scores_gemma":[0.0002047392,0.0001195805,0.0001550985,0.002510444,0.00008207529,0.0004729273,0.0004402257,0.0002228702,0.00001237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004718513,"about_ca_system_score_gemma":0.00009349424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003743908,"about_ca_topic_score_gemma":0.00001559466,"domain_scores_codex":[0.9983326,0.0003467539,0.0005451019,0.0003643005,0.0002217425,0.0001895272],"domain_scores_gemma":[0.9965079,0.000409457,0.000251466,0.002401059,0.0002664543,0.0001636654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001126355,0.001317663,0.601808,0.0006992234,0.002553511,0.000001798713,0.02368265,0.1599545,0.004956902,0.08164036,0.004357252,0.1189155],"study_design_scores_gemma":[0.0001526421,0.00005625178,0.05541495,0.00001835554,0.0002393434,0.000001073017,0.0001539829,0.9388547,0.00010916,0.00001009797,0.004867746,0.0001217298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08590584,0.0001415993,0.8971274,0.01497371,0.00003802368,0.0002142421,0.0000451648,0.001110752,0.0004432276],"genre_scores_gemma":[0.9088363,0.00002118213,0.09000828,0.0006433725,0.00002142816,0.00003643386,0.0004189558,0.000009430235,0.000004598764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8229305,"threshold_uncertainty_score":0.4876351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1187793596545258,"score_gpt":0.4134381074534665,"score_spread":0.2946587477989407,"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."}}