{"id":"W2162066572","doi":"10.1111/gcb.12652","title":"Terrestrial gross primary production inferred from satellite fluorescence and vegetation models","year":2014,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":209,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Lawrence Berkeley National Laboratory; Oak Ridge National Laboratory; Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences; Canadian Forest Service; University of Virginia; Climate Extremes; Università degli Studi della Tuscia; National Aeronautics and Space Administration","keywords":"Primary production; Environmental science; Atmospheric sciences; Carbon cycle; Latitude; Vegetation (pathology); Climatology; Deciduous; Ecology; Ecosystem; Geography; Physics; Geology","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":[0.00009682169,0.0001330209,0.0001341357,0.000002639684,0.00006978134,0.00001185331,0.0001058813,0.0001214177,0.00002336311],"category_scores_gemma":[0.00001518233,0.0001197232,0.0000226883,0.00007400665,0.0003010663,0.0002279568,0.000170345,0.00005810162,0.0000591082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000184321,"about_ca_system_score_gemma":0.000002171774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001606943,"about_ca_topic_score_gemma":0.0001111539,"domain_scores_codex":[0.9990921,0.00007237439,0.0001367659,0.0003879367,0.00009216805,0.0002186368],"domain_scores_gemma":[0.9996868,0.00001326312,0.00006742782,0.0001574219,0.000001399724,0.00007371965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008599665,0.00004726498,0.6388204,0.000005060872,0.000009149894,0.000001053228,0.0002924438,0.0005537833,0.002879671,0.0002653844,0.00003755277,0.3570022],"study_design_scores_gemma":[0.0003599216,0.0001282768,0.9631284,0.000008464492,0.0000150777,0.000006474108,0.00002129213,0.01958101,0.00002461216,0.01388761,0.002640366,0.0001984736],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907504,0.0002595761,0.006509355,0.0001956798,0.0005195076,0.0002209732,0.00000745146,0.00004469791,0.001492301],"genre_scores_gemma":[0.9914088,0.0003582982,0.007506738,0.0003155805,0.0002882904,0.00002280434,0.0000710263,0.000006334889,0.00002208944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3568037,"threshold_uncertainty_score":0.4882172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159361129025196,"score_gpt":0.2234379573450907,"score_spread":0.2018443460548388,"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."}}