{"id":"W2908510269","doi":"10.4000/books.aaccademia.4577","title":"Fully Convolutional Networks for Text Classification","year":2018,"lang":"en","type":"preprint","venue":"Accademia University Press eBooks","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Università degli Studi di Napoli Federico II","keywords":"Emoji; Computer science; Task (project management); Convolutional neural network; Artificial intelligence; Machine learning; Data mining; World Wide Web; Social media; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007087752,0.0013878,0.0005746095,0.001046846,0.0004199584,0.001219678,0.001349391,0.001529196,0.008385444],"category_scores_gemma":[0.003245919,0.0004803348,0.0008248955,0.001463302,0.0004232276,0.002746029,0.0008799701,0.002007497,0.005144646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001302419,"about_ca_system_score_gemma":0.000884648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01035369,"about_ca_topic_score_gemma":0.01581301,"domain_scores_codex":[0.9994143,0.0001229576,0.00003258996,0.0001945044,0.0001398602,0.00009570815],"domain_scores_gemma":[0.9991234,0.0003913871,0.00007553301,0.0001777763,0.0001946983,0.0000371698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000301947,0.0001692501,0.001545944,0.0004789618,0.0002409407,0.0002030308,0.0001501027,0.1649423,0.02129359,0.04834472,0.05383888,0.7084903],"study_design_scores_gemma":[0.00001154347,0.00003669315,0.0008446738,0.00004920079,0.00003258754,0.00005317551,0.00001761168,0.9282354,0.005437175,0.05193752,0.01332071,0.00002376863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02185483,0.006394219,0.945457,0.002441761,0.0008051749,0.0001032671,0.002677065,0.007129813,0.01313694],"genre_scores_gemma":[0.5885596,0.00568486,0.3330777,0.001356595,0.001244076,0.0003363663,0.01121549,0.0008065591,0.05771871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01035369,"threshold_uncertainty_score":0.02805215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0656289605154812,"score_gpt":0.2576380090301897,"score_spread":0.1920090485147085,"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."}}