{"id":"W3177344253","doi":"10.48550/arxiv.2106.14742","title":"TENT: Tensorized Encoder Transformer for Temperature Forecasting","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Transformer; Computer science; Encoder; Weather prediction; Exploit; Weather Research and Forecasting Model; Deep learning; Artificial intelligence; Artificial neural network; Weather forecasting; Recurrent neural network; Machine learning; Meteorology; Data mining; Geography; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000431606,0.0008104448,0.0003502145,0.0004155441,0.000205952,0.0005674062,0.0008874679,0.0004623065,0.003738252],"category_scores_gemma":[0.001952807,0.0002546442,0.000467229,0.0005653274,0.0003267144,0.001489952,0.0006671574,0.001067336,0.001277197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007524069,"about_ca_system_score_gemma":0.001099345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0102996,"about_ca_topic_score_gemma":0.01395874,"domain_scores_codex":[0.9998473,0.00002945033,0.00001135791,0.00004993896,0.00003570515,0.00002620966],"domain_scores_gemma":[0.9996019,0.0001515207,0.00004528829,0.00007027244,0.0001028412,0.00002825759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004533671,0.0001405214,0.003568579,0.0001784908,0.000106987,0.0001932201,0.0001342805,0.5675401,0.01584607,0.02807145,0.01461782,0.3691491],"study_design_scores_gemma":[0.000004362807,0.00002098962,0.0001517541,0.000003948624,0.000006717425,0.00001748686,0.000004564454,0.9910028,0.002109425,0.005729453,0.0009435894,0.000004957561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03040758,0.0005817969,0.9572257,0.0004328407,0.0001999948,0.00005458529,0.001281043,0.006328573,0.003488023],"genre_scores_gemma":[0.8230892,0.0006372722,0.1647708,0.0002480295,0.0001160894,0.0001128091,0.002315409,0.0003146259,0.008395704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0102996,"threshold_uncertainty_score":0.02047926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1012681167983705,"score_gpt":0.1853859024813343,"score_spread":0.08411778568296378,"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."}}