{"id":"W2110483583","doi":"10.1287/opre.2015.1349","title":"Technical Note—Trading Off Quick versus Slow Actions in Optimal Search","year":2015,"lang":"en","type":"article","venue":"Operations Research","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Iterative deepening depth-first search; Set (abstract data type); Search algorithm; Order (exchange); Computer science; Beam stack search; Search engine; Search problem; Search cost; Linear search; Best-first search; Beam search; Algorithm; Information retrieval; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003321874,0.0001115257,0.000139392,0.0008389642,0.0005336117,0.0008031879,0.001114911,0.0001243778,0.0001181294],"category_scores_gemma":[0.0008331462,0.0001095427,0.00004032931,0.002724499,0.0001954534,0.001167749,0.0005656601,0.0009825297,0.0005002494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005187173,"about_ca_system_score_gemma":0.001372736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003868499,"about_ca_topic_score_gemma":0.0008322579,"domain_scores_codex":[0.9966734,0.0006238931,0.000291679,0.0004872938,0.001222382,0.0007013715],"domain_scores_gemma":[0.9978011,0.0003103095,0.000008331345,0.0006825631,0.000795231,0.0004024335],"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":[0.0001231457,0.000737496,0.0002415195,0.00001513089,0.00001184769,0.00006882645,0.004853386,0.6942474,0.006171435,0.2403559,0.01750903,0.03566493],"study_design_scores_gemma":[0.001061541,0.0002723187,0.0001471874,0.00001484991,6.946721e-7,0.00001080706,0.000370175,0.987769,0.0005207555,0.00009559831,0.009603978,0.000133057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01226161,0.000127158,0.9169843,0.01862567,0.0004332075,0.001094785,0.000007859181,0.0002573821,0.05020805],"genre_scores_gemma":[0.7181218,0.00008325345,0.277724,0.00005180444,0.000110845,0.0001950387,0.00001964264,0.0000190916,0.003674583],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7058601,"threshold_uncertainty_score":0.7745159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2697715929038695,"score_gpt":0.4593099383107204,"score_spread":0.1895383454068509,"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."}}