{"id":"W4246558970","doi":"10.36227/techrxiv.12100692","title":"Deep Learning for text in limted data settings","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Artificial intelligence; Sequence (biology); Deep learning; Transfer of learning; Sequence learning; Natural language processing; Recurrent neural network; Machine learning; Sentiment analysis; Artificial neural network","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":[0.002402295,0.001139124,0.0008336513,0.001213298,0.0005791528,0.002104741,0.00188061,0.002359767,0.01123752],"category_scores_gemma":[0.0100676,0.0006126419,0.000777169,0.001642792,0.0009104637,0.00533956,0.002412104,0.00391023,0.004486683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001692885,"about_ca_system_score_gemma":0.001048119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005268581,"about_ca_topic_score_gemma":0.009025929,"domain_scores_codex":[0.9988223,0.0004470586,0.00008738174,0.00029333,0.0002385825,0.0001112879],"domain_scores_gemma":[0.9972805,0.001648558,0.0001699138,0.0004443341,0.0003651636,0.00009153644],"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":[0.0003561153,0.000313937,0.002425992,0.0009290324,0.0001896466,0.000513524,0.0002317178,0.2714675,0.004538769,0.1238743,0.05348814,0.5416713],"study_design_scores_gemma":[0.0000143395,0.0000300679,0.0003075218,0.00004453251,0.000008001733,0.00004665543,0.00002872829,0.8908819,0.001152188,0.09999159,0.007484122,0.00001032169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01023357,0.002729765,0.9730117,0.003458361,0.0003149846,0.0001259096,0.002297845,0.002832853,0.004995075],"genre_scores_gemma":[0.4108974,0.00621662,0.5476781,0.001441951,0.0009396685,0.0007786052,0.009045507,0.0008122283,0.02218995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01123752,"threshold_uncertainty_score":0.03759331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05509319530841789,"score_gpt":0.3401015580858983,"score_spread":0.2850083627774804,"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."}}