{"id":"W4403854166","doi":"10.1016/j.atech.2024.100619","title":"Innovative multi-temporal evapotranspiration forecasting using empirical fourier decomposition and bidirectional long short-term memory","year":2024,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Nature Conservancy of Canada; University of Saskatchewan; University of Guelph; University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada; University of Prince Edward Island; Atlantic Canada Opportunities Agency","keywords":"Evapotranspiration; Term (time); Long short term memory; Decomposition; Computer science; Long memory; Environmental science; Econometrics; Mathematics; Artificial intelligence; Artificial neural network; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009925623,0.000145838,0.0001161749,0.0001210501,0.0001892379,0.00005837777,0.00007025115,0.000192695,0.000044597],"category_scores_gemma":[0.000008135393,0.0001042252,0.00003291526,0.0008040084,0.0001550823,0.0003407943,0.00008491345,0.0002436221,0.00001735124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001745402,"about_ca_system_score_gemma":0.000008657043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003592129,"about_ca_topic_score_gemma":0.0003027376,"domain_scores_codex":[0.9991111,0.00002329159,0.0002094424,0.0003202851,0.0001365697,0.0001992891],"domain_scores_gemma":[0.9998245,0.00001958198,0.00003168692,0.00006647471,0.00002188585,0.0000359284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009617126,0.00005231817,0.8415181,0.00002089488,0.00005034421,0.00007378202,0.0003097174,0.002015671,0.1300009,0.0002370083,0.00005808827,0.02565352],"study_design_scores_gemma":[0.0003093757,0.0001375748,0.3978088,0.0001250459,0.00007097387,0.001713549,0.0001073603,0.5891696,0.00867111,0.0008268896,0.000506541,0.0005532386],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905698,0.00009908847,0.008048203,0.0003945477,0.0002032204,0.0001929165,0.00001282394,0.000225918,0.000253527],"genre_scores_gemma":[0.9905594,0.000006683379,0.009081444,0.00002000948,0.00004380464,0.00001881673,0.0001119933,0.000008372733,0.0001494876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5871539,"threshold_uncertainty_score":0.4250182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03428752142776448,"score_gpt":0.2819386743337067,"score_spread":0.2476511529059423,"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."}}