{"id":"W4393152373","doi":"10.1609/aaai.v38i21.30515","title":"Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Matching (statistics); Computer science; Mathematics education; Mathematical optimization; Mathematics; Statistics","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":[0.0009685453,0.0002291985,0.0002366996,0.0003353291,0.0003086232,0.0006930815,0.001429178,0.00007163736,0.00003189694],"category_scores_gemma":[0.0002580029,0.0001826844,0.0001017081,0.001123394,0.00007219276,0.0004072241,0.0005754176,0.000826203,0.0001116224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001078888,"about_ca_system_score_gemma":0.0001227353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002956327,"about_ca_topic_score_gemma":0.0000402353,"domain_scores_codex":[0.9978323,0.00002137714,0.0005517827,0.0005911654,0.0005202772,0.0004830894],"domain_scores_gemma":[0.9992374,0.0001245328,0.0001417609,0.0001926325,0.0001987851,0.0001048899],"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":[0.0000118202,0.0001459775,0.0001053509,0.0001079295,0.000018792,0.000003214245,0.01511823,0.4205437,0.06125813,0.3894154,0.00001965774,0.1132518],"study_design_scores_gemma":[0.0000147146,0.0001234135,0.0002314341,0.001284322,0.000007923662,0.00000978558,0.0009682713,0.9450742,0.005741583,0.04625246,0.00005551759,0.000236358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6409011,0.00008164536,0.3508042,0.004064137,0.0009798626,0.0005805175,0.000002060252,0.0002854965,0.002300962],"genre_scores_gemma":[0.9936883,0.000006542374,0.005931212,0.00006350358,0.0001000035,0.00002724331,2.266642e-7,0.00001774806,0.0001652565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5245305,"threshold_uncertainty_score":0.7449656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06907772291841656,"score_gpt":0.3283144772424974,"score_spread":0.2592367543240808,"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."}}