{"id":"W2783265852","doi":"10.1002/ecs2.2046","title":"Modeling leaf area index in North America using a process‐based terrestrial ecosystem model","year":2018,"lang":"en","type":"article","venue":"Ecosphere","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Michigan; National Science Foundation","keywords":"Leaf area index; Environmental science; Tundra; Deserts and xeric shrublands; Evergreen; Deciduous; Terrestrial ecosystem; Temperate forest; Temperate rainforest; Taiga; Boreal; Shrubland; Forestry; Atmospheric sciences; Ecosystem; Geography; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009347515,0.0002248379,0.0002318192,0.00001984366,0.000137393,0.00005532601,0.0002797401,0.0001239391,0.0004248338],"category_scores_gemma":[0.00003521827,0.00018578,0.0000603466,0.0005290581,0.00007509116,0.0002378982,0.0000839371,0.000221496,0.000380315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00037252,"about_ca_system_score_gemma":0.00005555303,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009956941,"about_ca_topic_score_gemma":0.03004538,"domain_scores_codex":[0.9983819,0.00004663712,0.0003178568,0.0004870775,0.0003508129,0.000415747],"domain_scores_gemma":[0.9994515,0.00001367639,0.0001089082,0.0003031777,0.00001501196,0.0001077867],"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":[0.00003845366,0.00004513569,0.00609056,0.000007386588,0.000003466925,0.00000871436,0.0004104887,0.9910744,0.0003658334,1.317558e-7,0.0007608964,0.001194526],"study_design_scores_gemma":[0.0004525918,0.00003540898,0.001219467,0.00005878,0.000007464085,0.00000593972,0.0001734835,0.9974381,0.0001350662,0.00005925839,0.0001557453,0.0002587259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9632382,0.000008240618,0.02634988,0.00005202564,0.0001478674,0.0002877429,0.00000640522,0.00007362836,0.009835956],"genre_scores_gemma":[0.9909295,0.00000121598,0.008637097,0.0001425008,0.0001631057,0.00000267153,0.000008577378,0.00002694114,0.00008838371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02904969,"threshold_uncertainty_score":0.9876538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018662915071878,"score_gpt":0.2322420680879575,"score_spread":0.2120554389372387,"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."}}