{"id":"W2007979101","doi":"10.1016/s0022-0000(03)00075-8","title":"Solving large FPT problems on coarse-grained parallel machines","year":2003,"lang":"en","type":"article","venue":"Journal of Computer and System Sciences","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; Dalhousie University; Carleton University","funders":"","keywords":"Computer science; Vertex cover; Parallelism (grammar); Bounded function; Vertex (graph theory); Implementation; Cover (algebra); Sequence (biology); Parallel computing; Tree (set theory); Parallel algorithm; Algorithm; Theoretical computer science; Mathematics; Graph; Combinatorics; Approximation algorithm; Programming language","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":[0.001624984,0.0008682701,0.001556583,0.0007989366,0.001358672,0.001811013,0.001666481,0.001727907,0.005499913],"category_scores_gemma":[0.01025569,0.0006660533,0.0008166677,0.001681596,0.001468045,0.003415003,0.001297601,0.00207283,0.0006886366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095068,"about_ca_system_score_gemma":0.001760738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005860194,"about_ca_topic_score_gemma":0.007488388,"domain_scores_codex":[0.9989623,0.0002273639,0.00009119409,0.0002346846,0.0002749205,0.0002094413],"domain_scores_gemma":[0.9924758,0.005810309,0.0002649382,0.0008857927,0.0003809583,0.0001821682],"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":[0.0005478986,0.0002415232,0.002088162,0.0004613429,0.0001094063,0.0003984853,0.0002238305,0.7973028,0.006186997,0.0375796,0.01263789,0.1422221],"study_design_scores_gemma":[0.0001017018,0.00004377378,0.0002464396,0.00001020512,0.0000148545,0.00005358317,0.00006230189,0.9387869,0.002349291,0.05723299,0.001090294,0.000007719344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4063807,0.002262314,0.5647924,0.00361778,0.0006678279,0.0001954641,0.0004967668,0.004907414,0.01667931],"genre_scores_gemma":[0.6324877,0.0004414269,0.3607965,0.0002975767,0.0002520003,0.0002308543,0.0006558288,0.0003605977,0.004477523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005860194,"threshold_uncertainty_score":0.018399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01946566761545364,"score_gpt":0.2503981644689971,"score_spread":0.2309324968535434,"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."}}