{"id":"W4411980074","doi":"10.1016/j.isprsjprs.2025.06.033","title":"Estimating carbon fluxes over North America using a physics-constrained deep learning model","year":2025,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"European Research Council; Horizon 2020 Framework Programme; National Natural Science Foundation of China","keywords":"Deep learning; Carbon fibers; Environmental science; Meteorology; Climatology; Geography; Remote sensing; Physical geography; Computer science; Atmospheric sciences; Artificial intelligence; Geology; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002284437,0.0005690638,0.0003550224,0.0003904937,0.0004001689,0.0006512027,0.0006349685,0.001018266,0.001416142],"category_scores_gemma":[0.001040762,0.0005172101,0.0004729065,0.0006615698,0.0004764684,0.0009572182,0.0004183191,0.0009395437,0.0002033709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001155346,"about_ca_system_score_gemma":0.001584467,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1208575,"about_ca_topic_score_gemma":0.1295868,"domain_scores_codex":[0.99994,0.000009949538,0.000002437698,0.00002818107,0.000007240597,0.00001213215],"domain_scores_gemma":[0.9997285,0.0001428312,0.00003096104,0.00002052386,0.00004977746,0.00002734158],"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":[0.00002794744,0.00002957592,0.002895277,0.000009193458,0.00003078203,0.00002596258,0.000009592724,0.9877813,0.0005096872,0.0007790115,0.0004512171,0.007450562],"study_design_scores_gemma":[0.00000413501,0.000002280901,0.0006558364,0.000001343561,0.000003029323,0.000002197565,0.000003594136,0.9982451,0.00007447432,0.0009373633,0.00006850651,0.000002146236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9104101,0.0006578367,0.0801622,0.001654004,0.0001001179,0.00002098637,0.001054612,0.0009194349,0.005020677],"genre_scores_gemma":[0.9888205,0.000115354,0.009118475,0.00008292665,0.00003372387,0.0000120042,0.0004362162,0.00003027038,0.001350457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8791425,"threshold_uncertainty_score":0.240308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006463857557721038,"score_gpt":0.2267556105512142,"score_spread":0.2202917529934932,"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."}}