{"id":"W2573425638","doi":"","title":"Distraction-based neural networks for modeling documents","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Automatic summarization; Computer science; Distraction; GRASP; Artificial neural network; Representation (politics); Artificial intelligence; Natural language processing; Traverse; Information retrieval; Machine learning; 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":[0.0003115437,0.0002030557,0.0001629959,0.0001580984,0.0001299348,0.0003428679,0.001043647,0.00008102733,0.0001531627],"category_scores_gemma":[0.0002422338,0.0001576956,0.0001360724,0.000104351,0.00005272922,0.0005691026,0.0001063011,0.0001405072,0.00009468026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001495774,"about_ca_system_score_gemma":0.00008880372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004760019,"about_ca_topic_score_gemma":0.0000317321,"domain_scores_codex":[0.9979714,0.00004021625,0.0005746608,0.0006239751,0.0004459714,0.0003438058],"domain_scores_gemma":[0.9985791,0.0002374888,0.0001661125,0.0004440826,0.0004571044,0.0001160734],"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":[0.00004227126,0.00006573751,0.00002293974,0.000002194937,0.00001245929,0.000003574144,0.00002926405,0.2124639,0.001530853,0.473153,0.00002291078,0.312651],"study_design_scores_gemma":[0.00006358532,0.00006938197,0.00001097953,0.00007660041,0.000002862962,0.000002563669,0.00001778565,0.9050566,0.00663205,0.08776002,0.0001147802,0.0001928223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005457317,0.000005052526,0.979928,0.01093332,0.002386573,0.0002496396,0.00001181902,0.0001558842,0.0008724429],"genre_scores_gemma":[0.9827872,0.000009129488,0.01608544,0.0005505535,0.0003199113,0.00008270427,0.000006687403,0.00001241124,0.0001459515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9773299,"threshold_uncertainty_score":0.6430639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1398677867114096,"score_gpt":0.3404630588579287,"score_spread":0.2005952721465191,"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."}}