{"id":"W2560628765","doi":"10.1016/j.agrformet.2016.12.001","title":"Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy","year":2016,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Youth Innovation Promotion Association of the Chinese Academy of Sciences; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Enhanced vegetation index; Normalized Difference Vegetation Index; Photosynthetically active radiation; Primary production; Environmental science; Canopy; Vegetation (pathology); Leaf area index; Remote sensing; Atmospheric sciences; Flux (metallurgy); Mean squared error; Photosynthesis; Ecosystem; Mathematics; Ecology; Vegetation Index; Statistics; Geography; Botany; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.0000823119,0.0001035985,0.0002085272,0.00002474292,0.00002600405,0.000003522125,0.00007139904,0.00008845863,0.00001136797],"category_scores_gemma":[0.00003870585,0.00005117915,0.00003557627,0.0001300112,0.0001380866,0.0001966115,0.00006158742,0.00005676499,0.00000443072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005273994,"about_ca_system_score_gemma":0.000002029884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002459361,"about_ca_topic_score_gemma":0.0015894,"domain_scores_codex":[0.9990821,0.00008371086,0.0002877451,0.0002802811,0.0001274293,0.0001387545],"domain_scores_gemma":[0.9996257,0.00005599116,0.0001621811,0.0001051849,0.00002269366,0.00002818489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004992454,0.00003125427,0.07052077,0.000009488157,0.000009924884,5.214796e-7,0.0003371213,0.002018003,0.9239123,0.000004756854,0.00001754287,0.003088398],"study_design_scores_gemma":[0.0003877266,0.00006933767,0.9310334,0.00004342931,0.00002040737,0.000009738611,0.00004161215,0.003957368,0.0634018,0.0009405236,0.000005874062,0.00008881467],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986988,0.00003813104,0.0001583348,0.0006061986,0.0001378528,0.0002321179,0.000003884224,0.000009636909,0.0001150661],"genre_scores_gemma":[0.9984313,0.00002530818,0.001407819,0.00002519578,0.00003346922,0.000004151137,0.00003109893,0.00000371122,0.00003793501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8605126,"threshold_uncertainty_score":0.3717834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120299507566033,"score_gpt":0.1971295755405381,"score_spread":0.1850996247839349,"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."}}