{"id":"W3017007515","doi":"10.4230/lipics.cpm.2020.11","title":"Summarizing Diverging String Sequences, with Applications to Chain-Letter Petitions","year":2020,"lang":"en","type":"preprint","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Automatic summarization; Heuristic; String (physics); Sequence (biology); Computer science; Tree (set theory); Variety (cybernetics); Set (abstract data type); Chain (unit); Algorithm; Theoretical computer science; Mathematics; Combinatorics; 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":[0.001350506,0.0009557215,0.001438726,0.003113928,0.001007653,0.001930129,0.001690201,0.001670135,0.002306244],"category_scores_gemma":[0.01079825,0.0005686518,0.0007623534,0.005575363,0.001069044,0.003257013,0.001237406,0.001721244,0.001341456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009847779,"about_ca_system_score_gemma":0.001022825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547607,"about_ca_topic_score_gemma":0.002496743,"domain_scores_codex":[0.9987285,0.0003076593,0.0001701998,0.0003796508,0.0003448569,0.00006906426],"domain_scores_gemma":[0.995091,0.002624541,0.0005577358,0.001048747,0.0005413845,0.0001365268],"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":[0.0004629979,0.000161371,0.00503339,0.0006060376,0.000114854,0.0005305154,0.0008112881,0.261052,0.01313764,0.05596959,0.01061708,0.6515034],"study_design_scores_gemma":[0.00004282601,0.000150449,0.00103125,0.00008170413,0.00004908252,0.0004992514,0.0002892611,0.8335392,0.01328213,0.1382959,0.0127016,0.00003735312],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05761655,0.002070619,0.9334292,0.0007027361,0.0001263076,0.0001746165,0.001297048,0.003050419,0.001532461],"genre_scores_gemma":[0.1655688,0.0007464422,0.8268556,0.0001543179,0.0001233791,0.0001570005,0.003832535,0.0003177624,0.002244188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003113928,"threshold_uncertainty_score":0.007715166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02790906043722501,"score_gpt":0.2671914755954198,"score_spread":0.2392824151581948,"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."}}