{"id":"W6921970873","doi":"10.1002/2015jg002997/abstract;jsessionid=0fe53dbe3502ab19b7d17e52f8c2d9e0.f04t01","title":"Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks","year":2015,"lang":"en","type":"other","venue":"Joint Research Centre (European Commission)","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of British Columbia","funders":"","keywords":"Extrapolation; Sampling (signal processing); Artificial neural network; Flux (metallurgy); Eddy covariance; Carbon cycle; Spatial variability; Vegetation (pathology)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004125073,0.0003555838,0.0008297662,0.0003994498,0.0002255131,0.0002111015,0.0003599503,0.00008857969,0.002558692],"category_scores_gemma":[0.0003739523,0.0002654568,0.0001169429,0.0001054664,0.0003251056,0.00006425643,0.0006419044,0.0004172893,0.000527029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008654202,"about_ca_system_score_gemma":0.00001827777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001212394,"about_ca_topic_score_gemma":0.00007221544,"domain_scores_codex":[0.9972559,0.0004060661,0.0006583199,0.0008408871,0.0001509754,0.0006878298],"domain_scores_gemma":[0.9985139,0.0002426937,0.0003603555,0.0005200053,0.00008961531,0.0002733917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005301338,0.00012083,0.00190631,0.0004233837,0.0004396765,0.0000604178,0.001463448,0.001135138,0.00006005193,0.00024849,0.9452232,0.04838899],"study_design_scores_gemma":[0.001826597,0.0005811529,0.0002076146,0.0006722998,0.00005491526,0.000002553823,0.0003732335,0.06493238,0.0001185139,0.0001479987,0.930419,0.0006637381],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006633241,0.001308644,0.000977963,0.0001489739,0.002034611,0.001047817,0.0007014525,0.0001008391,0.9870465],"genre_scores_gemma":[0.1108379,0.00009484019,0.002857098,0.00002721352,0.002838516,0.00001661546,0.0007502488,0.0007990579,0.8817785],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.105268,"threshold_uncertainty_score":0.9999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08217293687239442,"score_gpt":0.2784063429206383,"score_spread":0.1962334060482439,"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."}}