{"id":"W3163735098","doi":"10.48550/arxiv.2105.02923","title":"Hone as You Read: A Practical Type of Interactive Summarization","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Automatic summarization; Computer science; Heuristics; Task (project management); Reading (process); Process (computing); Variety (cybernetics); Metric (unit); Code (set theory); Human–computer interaction; Relevance (law); Artificial intelligence; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001771503,0.000181884,0.0002837529,0.0001785536,0.00004730335,0.00008682355,0.0007535926,0.0002331469,0.00008029368],"category_scores_gemma":[0.0002863073,0.0002222109,0.0001133274,0.0005394892,0.0000515866,0.0005709014,0.002009106,0.0005252858,0.0000348766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001593733,"about_ca_system_score_gemma":0.0005441009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003690086,"about_ca_topic_score_gemma":0.00003183621,"domain_scores_codex":[0.9984747,0.0001723542,0.0002035731,0.0008501788,0.0001128722,0.0001862961],"domain_scores_gemma":[0.9979185,0.0001468405,0.0003103878,0.001071118,0.0004596304,0.00009351325],"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":[0.0002753596,0.0007583391,0.00559142,0.0003382734,0.0006018553,0.002233075,0.005519687,0.2309853,0.001216617,0.7461919,0.0003013751,0.005986784],"study_design_scores_gemma":[0.0004317672,0.0001082899,0.0007401058,0.0002797443,0.0001118986,0.00002513947,0.000618119,0.9679155,0.001295389,0.0275974,0.0004084149,0.0004682831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2276978,0.00002380762,0.7662476,0.0001592487,0.00069565,0.000134083,0.000002265421,0.00007999681,0.004959635],"genre_scores_gemma":[0.9829867,0.0001027981,0.01534066,0.00006316276,0.00005012012,3.127991e-7,0.00002201854,0.00001077683,0.001423456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.755289,"threshold_uncertainty_score":0.9061499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1190926273060999,"score_gpt":0.2408938892290226,"score_spread":0.1218012619229227,"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."}}