{"id":"W3017097095","doi":"10.1016/j.dam.2020.04.001","title":"Foreword: Eighth Workshop on Graph Classes, Optimization, and Width Parameters, Toronto, Ontario, Canada","year":2020,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Combinatorics; Graph; Property (philosophy); Enhanced Data Rates for GSM Evolution; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001577524,0.0002806007,0.0003287948,0.00003704705,0.0001822581,0.0001442751,0.0008105954,0.00007430456,0.00007641701],"category_scores_gemma":[0.00006293987,0.0002422393,0.00004839951,0.0003330819,0.00009023373,0.0002739862,0.0003099118,0.0002605426,0.000005058846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002154139,"about_ca_system_score_gemma":0.0002676394,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02658291,"about_ca_topic_score_gemma":0.3761815,"domain_scores_codex":[0.9980782,0.00002450743,0.0003469005,0.0005381036,0.0005669364,0.0004453395],"domain_scores_gemma":[0.9984545,0.0003466445,0.0001480119,0.0006587384,0.00005250286,0.0003395934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003982746,0.00005569726,0.00005975118,0.0001097128,0.00007725515,0.00001958805,0.003529649,0.0178133,0.00006492694,0.9708633,0.003260582,0.004106343],"study_design_scores_gemma":[0.002134734,0.0003848663,0.0002807843,0.000205928,0.00008093918,0.00002307548,0.002951569,0.5720544,0.001829404,0.4114138,0.006685434,0.001955024],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00442855,0.0001106553,0.9862281,0.0008735611,0.00006591195,0.0005657248,0.00001568862,0.0001317488,0.007580024],"genre_scores_gemma":[0.1833944,0.00007796288,0.814631,0.001484791,0.00003314138,0.00009087748,0.00002395312,0.0000467758,0.0002171504],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5594496,"threshold_uncertainty_score":0.9878233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051645569089614,"score_gpt":0.2447658754428478,"score_spread":0.2242494197519516,"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."}}