{"id":"W2809867153","doi":"10.1609/icaps.v28i1.13885","title":"MS-Lite: A Lightweight, Complementary Merge-and-Shrink Method","year":2018,"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 Alberta","funders":"","keywords":"Merge (version control); Heuristics; Computer science; Algorithm; Parallel computing","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":[0.001922864,0.001201109,0.001148325,0.002145255,0.000911765,0.001299834,0.002763351,0.001218258,0.009785345],"category_scores_gemma":[0.00648401,0.0009757422,0.001962795,0.001702776,0.001126895,0.003337667,0.004191932,0.002250745,0.002708851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007958653,"about_ca_system_score_gemma":0.002384918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002335209,"about_ca_topic_score_gemma":0.005862273,"domain_scores_codex":[0.9985893,0.0003266295,0.0001258023,0.0002403055,0.0005648136,0.000152995],"domain_scores_gemma":[0.9978957,0.0009539496,0.0001723188,0.0005871058,0.0002722947,0.0001185139],"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":[0.0005978957,0.0001816033,0.001645095,0.0009274784,0.0001871524,0.0002632973,0.0005438928,0.06501036,0.01412896,0.03740505,0.04361885,0.8354904],"study_design_scores_gemma":[0.0004107941,0.0002491174,0.0007679373,0.0001394953,0.0001389995,0.0003555238,0.0002739718,0.8135482,0.02215341,0.07758121,0.08426635,0.0001149549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004142925,0.0002998492,0.9804968,0.000170446,0.00006350128,0.0001667827,0.0003465372,0.01165376,0.002659405],"genre_scores_gemma":[0.04740334,0.0001623954,0.9453778,0.0002399256,0.00004076307,0.0002807723,0.001080785,0.002144863,0.003269441],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009785345,"threshold_uncertainty_score":0.03273529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04241745071625335,"score_gpt":0.3183386825914925,"score_spread":0.2759212318752391,"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."}}