{"id":"W4388725836","doi":"10.1016/j.agee.2023.108810","title":"Partitioning eddy covariance CO2 fluxes into ecosystem respiration and gross primary productivity through a new hybrid four sub-deep neural network","year":2023,"lang":"en","type":"article","venue":"Agriculture Ecosystems & Environment","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Primary production; Eddy covariance; Environmental science; Atmospheric sciences; Ecosystem respiration; Covariance; Ecosystem; Mathematics; Ecology; Statistics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005118588,0.0003280275,0.0003374396,0.00001906588,0.000499106,0.0001411226,0.00018971,0.0001244652,0.00006951693],"category_scores_gemma":[0.0000130706,0.0002589399,0.00007974999,0.0003007231,0.00005595285,0.0006590444,0.0002636373,0.0002263372,0.0007401835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000436507,"about_ca_system_score_gemma":0.000008482921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004033297,"about_ca_topic_score_gemma":0.00141622,"domain_scores_codex":[0.9975522,0.0002058244,0.0004737067,0.0007712823,0.0004883553,0.0005086816],"domain_scores_gemma":[0.9991477,0.00005575462,0.0002621236,0.0003649969,0.000005544825,0.0001639257],"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":[0.00003782226,0.0001008034,0.05182701,0.0001159787,0.00007991128,0.0000912451,0.001079682,0.8684329,0.06012887,0.0004002856,0.01563173,0.002073793],"study_design_scores_gemma":[0.002262727,0.0004811138,0.3031386,0.0003822004,0.0002924054,0.001169279,0.0002554687,0.3456873,0.008329635,0.004282389,0.3310957,0.002623188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920167,0.0002057462,0.005133818,0.0006437551,0.0004570179,0.0008882743,0.00004232687,0.000176743,0.0004355689],"genre_scores_gemma":[0.9964787,0.0002000731,0.001232053,0.00009601572,0.0005724194,0.0001593878,0.0003825871,0.00002761839,0.0008511628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5227455,"threshold_uncertainty_score":0.9999863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008269994627618772,"score_gpt":0.1755576710676698,"score_spread":0.167287676440051,"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."}}