{"id":"W4249919375","doi":"10.32920/ryerson.14663754.v1","title":"Worst-case & average-case Efficiency trade-offs for search problems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Computer science; Set (abstract data type); Search theory; Operations research; Unit (ring theory); Work (physics); Mathematics; Engineering; Economics; Microeconomics","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001756593,0.0005244331,0.0005787866,0.0004239954,0.0005580165,0.002027123,0.001621112,0.0004492482,0.000394491],"category_scores_gemma":[0.0001810028,0.0004844394,0.0004251543,0.0008256614,0.00009096725,0.0004505995,0.002651427,0.001103516,0.00007227633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001505803,"about_ca_system_score_gemma":0.0009141671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002531124,"about_ca_topic_score_gemma":0.0003624823,"domain_scores_codex":[0.9953604,0.000315058,0.0007975122,0.001781217,0.0007442003,0.001001655],"domain_scores_gemma":[0.9964027,0.0004182362,0.0001752736,0.002009473,0.0004779312,0.0005163955],"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":[0.00002847739,0.001931814,0.00005386834,0.003085173,0.000290699,0.017311,0.0211478,0.8169855,0.000222721,0.06527095,0.008527177,0.06514482],"study_design_scores_gemma":[0.000840193,0.000115629,0.000003074757,0.0001614863,0.00001784596,0.008305556,0.0001793338,0.9814701,0.000228224,0.001735788,0.00617282,0.0007699567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002581661,0.0002733023,0.9861391,0.003309022,0.0008886373,0.002805834,0.00004065186,0.0005669519,0.003394825],"genre_scores_gemma":[0.2779088,0.0001517208,0.7099363,0.0008846365,0.0002359086,0.0008058528,0.0002185517,0.00008853911,0.009769742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2762029,"threshold_uncertainty_score":0.9997607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07756221973192895,"score_gpt":0.3166777557122791,"score_spread":0.2391155359803501,"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."}}