{"id":"W4388481814","doi":"10.48550/arxiv.2311.02891","title":"AdaFlood: Adaptive Flood Regularization","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Regularization (linguistics); Flood myth; Computer science; Sample (material); Generalization; Artificial intelligence; Artificial neural network; Asynchronous communication; Machine learning; Mathematics; Geography","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":[0.002261279,0.001430558,0.001221943,0.0009637314,0.0004953185,0.001195008,0.002529148,0.002125732,0.002463419],"category_scores_gemma":[0.005586247,0.0006124458,0.0009301224,0.0006501473,0.0009349581,0.0018985,0.002088331,0.00295042,0.001385289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007481995,"about_ca_system_score_gemma":0.001149007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002896083,"about_ca_topic_score_gemma":0.004599717,"domain_scores_codex":[0.999109,0.0002548427,0.00004843248,0.0002380446,0.0002583855,0.00009127117],"domain_scores_gemma":[0.9987019,0.0005443522,0.0001122085,0.0002451457,0.0003192166,0.00007727797],"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":[0.0005246432,0.0003777746,0.002631431,0.0002865846,0.0002687513,0.0001587655,0.0002386538,0.3564065,0.01202153,0.01217597,0.03313415,0.5817752],"study_design_scores_gemma":[0.00002981032,0.00007019667,0.0002218883,0.00001723508,0.0000111136,0.0000524778,0.00001560144,0.9846884,0.003488976,0.007816154,0.003573159,0.00001507918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01382981,0.000559533,0.9788252,0.0003683945,0.0001290357,0.00008463756,0.0002213942,0.004680505,0.001301603],"genre_scores_gemma":[0.3558914,0.0005166571,0.6277796,0.001133977,0.0003069507,0.0006633971,0.001628256,0.001446003,0.01063371],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002896083,"threshold_uncertainty_score":0.01195896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1266306671381526,"score_gpt":0.1839890548188255,"score_spread":0.05735838768067295,"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."}}