{"id":"W1594807353","doi":"10.1007/978-3-540-69338-3_18","title":"Evaluation of Offset Assignment Heuristics","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Computer science; Offset (computer science); Partition (number theory); Parallel computing; Computation; Heuristic; Overhead (engineering); Algorithm; Optimization problem; Permutation (music); Mathematics; Artificial intelligence","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":[0.003222654,0.00281862,0.002366676,0.002653526,0.001667009,0.002703273,0.003707644,0.002361488,0.01814449],"category_scores_gemma":[0.01095696,0.000906374,0.001062058,0.004103233,0.0009915167,0.002631198,0.00170126,0.00142585,0.001961639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004437355,"about_ca_system_score_gemma":0.003998481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01480909,"about_ca_topic_score_gemma":0.01523188,"domain_scores_codex":[0.9963665,0.001242918,0.0001634026,0.0006010056,0.0009466865,0.0006794346],"domain_scores_gemma":[0.9871257,0.009313375,0.0003976255,0.00117837,0.001519292,0.0004656738],"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":[0.005623592,0.002352781,0.00258482,0.000971386,0.0002644184,0.0001509958,0.0001612442,0.5806326,0.004435484,0.01082541,0.02714062,0.3648566],"study_design_scores_gemma":[0.0006119487,0.0005696634,0.0007508103,0.0000624488,0.0001141669,0.00005704261,0.0001631718,0.9857519,0.00430632,0.004382565,0.003204644,0.00002535805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7331902,0.008642058,0.1287124,0.001317913,0.001499977,0.001013964,0.00379181,0.01387703,0.1079545],"genre_scores_gemma":[0.7148678,0.001134859,0.2647822,0.0003631235,0.0001670412,0.0003134781,0.005506717,0.00172807,0.01113676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01814449,"threshold_uncertainty_score":0.06069934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04617933915119889,"score_gpt":0.2776434199617956,"score_spread":0.2314640808105967,"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."}}