{"id":"W2756655043","doi":"10.1609/icaps.v27i1.13807","title":"Boosting Search Guidance in Problems with Semantic Attachments","year":2017,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Planner; Exploit; Computer science; Heuristic; Estimator; Boosting (machine learning); Leverage (statistics); Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002577512,0.001286065,0.001572303,0.001105128,0.0008745935,0.001362667,0.001707937,0.002176135,0.00408179],"category_scores_gemma":[0.01215456,0.0006643748,0.0007722876,0.0009849966,0.001855307,0.002843885,0.002981544,0.002312591,0.0007445677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001282767,"about_ca_system_score_gemma":0.001735792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003497471,"about_ca_topic_score_gemma":0.004962069,"domain_scores_codex":[0.9987851,0.0004390092,0.00005761975,0.0001988199,0.0003670843,0.0001523982],"domain_scores_gemma":[0.9965203,0.002335171,0.0002185465,0.0003773895,0.0003646854,0.000183833],"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":[0.0002652561,0.0001659704,0.001513392,0.0002663728,0.00004489318,0.0001371273,0.0004044216,0.7262642,0.002397601,0.1342463,0.005614808,0.1286797],"study_design_scores_gemma":[0.00003140588,0.00005297083,0.0001294305,0.00002116895,0.00001171224,0.00002223197,0.00003235844,0.9321992,0.0005117735,0.065249,0.001732046,0.000006673428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05920121,0.0007054519,0.9281924,0.0008936102,0.00008747844,0.0001152139,0.0001346625,0.001504623,0.009165411],"genre_scores_gemma":[0.6478961,0.0004243259,0.3455304,0.000427861,0.0001489444,0.0002265613,0.0004911911,0.0002902437,0.004564441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00408179,"threshold_uncertainty_score":0.01365495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05653941684740136,"score_gpt":0.3161860120499285,"score_spread":0.2596465952025271,"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."}}