{"id":"W6891654700","doi":"10.4230/artifacts.22479","title":"torus packing for multisets","year":2024,"lang":"en","type":"other","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multiset; Dimension (graph theory); Torus; Integer (computer science); Conjecture; Grid; Property (philosophy)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003962806,0.0009615051,0.0008500524,0.001045324,0.0009783165,0.001939219,0.001268737,0.000905733,0.0191108],"category_scores_gemma":[0.002336916,0.0006298493,0.001833791,0.001421137,0.0007398038,0.002938444,0.002942192,0.001030175,0.006013874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204783,"about_ca_system_score_gemma":0.0008093849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001914831,"about_ca_topic_score_gemma":0.002519151,"domain_scores_codex":[0.9993196,0.00007456941,0.00005890044,0.0001847484,0.0002376331,0.0001246674],"domain_scores_gemma":[0.9991478,0.0003234633,0.00008372236,0.0002614514,0.0001129423,0.00007064223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006186737,0.0001888203,0.00461604,0.001140561,0.0001198832,0.0007402292,0.00102,0.1576509,0.0210959,0.197897,0.069897,0.545015],"study_design_scores_gemma":[0.00006236565,0.0001574051,0.0009011183,0.0001510874,0.00003260951,0.0005823272,0.0002761859,0.627628,0.02330506,0.2862656,0.06056995,0.00006827548],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03818657,0.0005118491,0.9049788,0.0003235368,0.0002427708,0.0001834016,0.002107972,0.03188177,0.02158328],"genre_scores_gemma":[0.2562357,0.0003841033,0.7213179,0.0002457876,0.00006807374,0.0003374915,0.006243638,0.00526773,0.009899495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0191108,"threshold_uncertainty_score":0.063932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02428410627725018,"score_gpt":0.3135549280345771,"score_spread":0.2892708217573269,"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."}}