{"id":"W4376277151","doi":"10.1287/ijoc.2022.0090","title":"Learning for Spatial Branching: An Algorithm Selection Approach","year":2023,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Branching (polymer chemistry); Algorithm; Christian ministry; Artificial intelligence; Machine learning; Context (archaeology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001091794,0.0002128419,0.0002234025,0.0004973405,0.001165329,0.0005982828,0.0005623228,0.00008633602,0.000002650867],"category_scores_gemma":[0.0002606692,0.000189628,0.0001189606,0.0009606098,0.00002286473,0.001589522,0.0001429725,0.0006905284,0.00002751854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001674693,"about_ca_system_score_gemma":0.0001071752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005691038,"about_ca_topic_score_gemma":6.203169e-7,"domain_scores_codex":[0.9981439,0.00007069763,0.0004760751,0.0003443873,0.0004364768,0.0005285189],"domain_scores_gemma":[0.9986244,0.0002169222,0.0004248639,0.0001679577,0.0003724245,0.0001934079],"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":[0.000006103745,0.00002207704,0.00006740605,0.000003781281,0.00001067124,0.000002881124,0.0007060454,0.5257746,0.00002351566,0.0003804554,0.00002265646,0.4729798],"study_design_scores_gemma":[0.0009484152,0.0005297323,0.0009374366,0.00002781999,0.000003196737,0.0002449247,0.0001638446,0.9943364,0.0002463663,0.0007914428,0.001519991,0.0002504719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004089713,0.000004349405,0.993479,0.00007286705,0.0009505271,0.0002535706,7.409471e-7,0.0006510469,0.0004981422],"genre_scores_gemma":[0.1452649,0.000007283706,0.8533604,0.000215577,0.0009203367,0.000007509389,0.0000168142,0.00003322274,0.0001740185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4727293,"threshold_uncertainty_score":0.896289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02176102297544207,"score_gpt":0.2951156799378986,"score_spread":0.2733546569624565,"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."}}