{"id":"W2760492644","doi":"10.1080/10556788.2019.1692344","title":"mts: a light framework for parallelizing tree search codes","year":2019,"lang":"en","type":"preprint","venue":"Optimization methods & software","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Japan Society for the Promotion of Science","keywords":"Computer science; Backtracking; Enumeration; Satisfiability; Debugging; Search tree; Parallel computing; Vertex (graph theory); Theoretical computer science; Branch and bound; Search algorithm; Algorithm; Graph; Programming language; Discrete mathematics; 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":[0.001699613,0.001275095,0.001077415,0.001575838,0.001216798,0.002107078,0.003189933,0.001096333,0.01045074],"category_scores_gemma":[0.006123513,0.001008338,0.002078663,0.001968342,0.00248573,0.003269209,0.003059889,0.002937691,0.003594257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001781782,"about_ca_system_score_gemma":0.002390055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00464805,"about_ca_topic_score_gemma":0.005282457,"domain_scores_codex":[0.9978325,0.0004988163,0.0002046193,0.0002690596,0.0009171923,0.0002778765],"domain_scores_gemma":[0.9975201,0.0007769004,0.0001960315,0.0008340459,0.0005140143,0.0001586704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006002251,0.0001775945,0.00133714,0.0006304067,0.0001002143,0.0004084515,0.0006365929,0.1767352,0.02420131,0.4796861,0.02503062,0.2904561],"study_design_scores_gemma":[0.0002135733,0.0001451901,0.0002664249,0.0001125413,0.00005733323,0.0002191002,0.00008906176,0.6332071,0.02504846,0.2494055,0.09116028,0.00007547394],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002259948,0.00008129245,0.9887335,0.00009955268,0.00004366406,0.0000861366,0.0001139714,0.006415589,0.002166359],"genre_scores_gemma":[0.06005082,0.0002352476,0.9307174,0.0001748933,0.00008642835,0.0006443101,0.0005103126,0.003687494,0.003893217],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01045074,"threshold_uncertainty_score":0.03496122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0714005721263264,"score_gpt":0.401624404560027,"score_spread":0.3302238324337006,"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."}}