{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005609752,0.001510496,0.001947956,0.002291046,0.0008732544,0.00189809,0.003571936,0.001998941,0.007820109],"category_scores_gemma":[0.01608875,0.0007792959,0.00176519,0.002144434,0.001497943,0.003203476,0.002971475,0.003343132,0.002216506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001397506,"about_ca_system_score_gemma":0.00286893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001295001,"about_ca_topic_score_gemma":0.001856885,"domain_scores_codex":[0.996116,0.001810086,0.0001913195,0.0006413794,0.0009006176,0.0003406031],"domain_scores_gemma":[0.9884968,0.008035735,0.0006857275,0.001035802,0.001298805,0.000447207],"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":[0.0003942661,0.0004384689,0.003716667,0.0002916078,0.0001540445,0.0002362805,0.0002604411,0.3370999,0.003022576,0.08126669,0.009467347,0.5636518],"study_design_scores_gemma":[0.00005409834,0.00009162191,0.00009977783,0.00002394583,0.00002453327,0.00004965417,0.00002259664,0.9669261,0.000716853,0.03055282,0.001430449,0.000007549384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007425815,0.0003412114,0.9878381,0.0004123391,0.00004459019,0.0001247619,0.00003896264,0.0008423353,0.002931788],"genre_scores_gemma":[0.2475755,0.0004766758,0.7451187,0.0006309056,0.0002632996,0.0005794351,0.0004045184,0.0004855675,0.004465419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007820109,"threshold_uncertainty_score":0.02966756,"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."}}