{"id":"W2071500750","doi":"10.1145/2739482.2764898","title":"Empirical Scaling Analyser","year":2015,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Analyser; Scaling; Solver; Heuristic; Empirical research; Key (lock); Time complexity; Computational complexity theory; Algorithm; Theoretical computer science; Artificial intelligence; 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":[0.0001118128,0.00003034672,0.00003687628,0.00004555119,0.00002188164,0.00006729484,0.0001128835,0.00001750046,0.00007622661],"category_scores_gemma":[0.00002816059,0.00002479736,0.00001757045,0.000215434,0.000009085125,0.0002226072,0.00004145758,0.00002789591,0.0001385807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001660359,"about_ca_system_score_gemma":0.00004425072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008293415,"about_ca_topic_score_gemma":0.000007278966,"domain_scores_codex":[0.9996356,0.0000175597,0.0000689161,0.0001025072,0.0001107117,0.00006474613],"domain_scores_gemma":[0.9997016,0.00001263449,0.00001288511,0.0001306801,0.0000546373,0.0000875559],"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":[0.000007657636,0.0001008635,0.2317422,0.000003625209,0.00003957879,0.00002374769,0.002896562,0.03453227,0.0001295784,0.1569397,0.07699936,0.4965848],"study_design_scores_gemma":[0.0002532268,0.00001476377,0.01514566,0.000001482811,0.000002340766,0.00001518583,0.00007791981,0.970493,0.0002963548,0.002251235,0.01134073,0.0001080892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003657872,0.000005362283,0.9580095,0.002121401,0.0001332725,0.00001776228,5.034567e-8,0.0001412853,0.0359135],"genre_scores_gemma":[0.802952,7.108425e-7,0.1952879,0.001038368,0.00002297188,8.323206e-7,6.077346e-7,0.000001400223,0.0006951589],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9359608,"threshold_uncertainty_score":0.1781221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08652012247492252,"score_gpt":0.3297659059274811,"score_spread":0.2432457834525586,"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."}}