{"id":"W4389867379","doi":"10.15485/2229439","title":"Improving the Estimation of the Atmospheric Water Vapor Pressure Using Interpretable Long Short-Term Memory Networks: Dataset, Python code, and trained models","year":2023,"lang":"en","type":"dataset","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Python (programming language); Long short term memory; Computer science; Estimation; Term (time); Code (set theory); Artificial intelligence; Artificial neural network; Data mining; Programming language; Engineering; Recurrent neural network; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081336,0.0002690347,0.0003799671,0.00003981001,0.0003666754,0.0001224932,0.0006531551,0.0002344571,0.00005914578],"category_scores_gemma":[0.0001363663,0.000152222,0.0000870619,0.0003960524,0.001459026,0.0007995507,0.001217833,0.0002327409,0.000005518288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004861349,"about_ca_system_score_gemma":0.00003235103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004736035,"about_ca_topic_score_gemma":0.0001239986,"domain_scores_codex":[0.9977425,0.00008215771,0.0008597772,0.0003522352,0.000642404,0.0003208735],"domain_scores_gemma":[0.9984558,0.0001595139,0.0005595341,0.0006980234,0.00004530143,0.00008184835],"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":[0.00006617468,0.00009522033,0.00006204209,0.0002460173,0.00005332217,0.000001235063,0.00001234031,0.867597,0.0005943268,0.0000284364,0.1293436,0.001900273],"study_design_scores_gemma":[0.0005423173,0.0002771481,0.004188106,0.0005977736,0.0006951586,0.00006730991,0.000009390661,0.9612381,0.001480218,0.0002322579,0.03002875,0.0006433962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2894163,0.0002461277,0.02010628,0.0001421731,0.001101755,0.001478268,0.6868817,0.0001191375,0.0005082357],"genre_scores_gemma":[0.6239874,0.00002905656,0.0009100225,0.00005154357,0.00001335117,0.00002425911,0.3749234,0.00001213903,0.00004874895],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.3345711,"threshold_uncertainty_score":0.6207434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01703620230873861,"score_gpt":0.2313982510455034,"score_spread":0.2143620487367647,"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."}}