{"id":"W2171410117","doi":"10.5614/ejgta.2015.3.1.2","title":"On scores, losing scores and total scores in hypertournaments","year":2015,"lang":"en","type":"article","venue":"Electronic Journal of Graph Theory and Applications","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Killam Trusts; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Combinatorics; Mathematics; Hypergraph; Element (criminal law); Vertex (graph theory); Enhanced Data Rates for GSM Evolution; Graph; Computer science; Artificial intelligence; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00207288,0.0001365578,0.0002015034,0.0004800729,0.000160826,0.0001040201,0.0004697162,0.00004398986,0.000002273129],"category_scores_gemma":[0.0001213155,0.0001123523,0.00005057785,0.0005794274,0.0002320124,0.0004695039,0.0001113248,0.0005349761,0.000002536238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007979701,"about_ca_system_score_gemma":0.0001929713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003003509,"about_ca_topic_score_gemma":0.000008393503,"domain_scores_codex":[0.9984559,0.0002716628,0.0003052067,0.0002390049,0.000296097,0.0004320726],"domain_scores_gemma":[0.9987997,0.0004432268,0.00017954,0.0002665014,0.0001082849,0.0002027399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001744805,0.00009791766,0.001186873,0.000005325046,0.00002542586,0.000007260331,0.0002057873,0.000165773,0.0008508074,0.9747347,0.00002290032,0.02252273],"study_design_scores_gemma":[0.0008075653,0.0004568298,0.001420106,0.00006398423,0.000007855809,0.0003208346,0.0001045319,0.0001603634,0.0008351575,0.9954955,0.0002092829,0.0001179862],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7448267,0.008062612,0.2461275,0.0003795705,0.00003987415,0.0002516441,0.000001680466,0.00001866025,0.000291716],"genre_scores_gemma":[0.9980975,0.0006945953,0.0009814397,0.00009824447,0.00005027293,0.00002083784,5.457428e-7,0.00001020845,0.00004638248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2532707,"threshold_uncertainty_score":0.4581593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486821573934679,"score_gpt":0.2869802082873247,"score_spread":0.272111992547978,"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."}}