{"id":"W2805309537","doi":"10.1002/rsa.20760","title":"The stripping process can be slow: Part I","year":2018,"lang":"en","type":"article","venue":"Random Structures and Algorithms","topic":"Stochastic processes and statistical mechanics","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hypergraph; Combinatorics; Vertex (graph theory); Mathematics; Constant (computer programming); Integer (computer science); Stripping (fiber); Degree (music); Upper and lower bounds; Discrete mathematics; Physics; Computer science; Mathematical analysis; Graph; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003915464,0.000839122,0.001213104,0.0007216044,0.001390328,0.002042429,0.001942481,0.001372343,0.005964149],"category_scores_gemma":[0.02079662,0.0006729689,0.001303625,0.0006731651,0.003342327,0.004544433,0.002549318,0.003043996,0.0008005671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502532,"about_ca_system_score_gemma":0.001824034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00242593,"about_ca_topic_score_gemma":0.001689277,"domain_scores_codex":[0.9982234,0.0004385945,0.0001045647,0.0004678651,0.0003542017,0.0004113718],"domain_scores_gemma":[0.9820225,0.01020424,0.001652057,0.004092604,0.001169812,0.0008587618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001522406,0.0003589336,0.006024475,0.0007706796,0.0002070562,0.0007541202,0.000508083,0.5934892,0.02985644,0.2980549,0.006391919,0.06206178],"study_design_scores_gemma":[0.00007074996,0.0001463703,0.0009082218,0.00004521593,0.00005397866,0.000204192,0.0001067018,0.8695908,0.0101609,0.1164269,0.002254287,0.00003167534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.307843,0.001228604,0.6690814,0.001798674,0.0002020985,0.0003066842,0.0002656215,0.00111365,0.01816028],"genre_scores_gemma":[0.9177159,0.0004745016,0.07410678,0.0003147404,0.0001158594,0.0002217643,0.0001513239,0.0003034676,0.006595783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005964149,"threshold_uncertainty_score":0.02070719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03176608976674083,"score_gpt":0.3183951669337252,"score_spread":0.2866290771669843,"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."}}