{"id":"W2337454614","doi":"","title":"An update on the globcarbon initiative: Multi-sensor estimation of global biophysical products for global terrestrial carbon studies","year":2007,"lang":"en","type":"article","venue":"UCL Discovery (University College London)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Vegetation (pathology); Carbon cycle; Earth observation; Environmental science; Global change; Remote sensing; Meteorology; Climatology; Computer science; Climate change; Geography; Satellite; Ecosystem; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004630676,0.001651245,0.0007500384,0.003167915,0.0004502324,0.003958253,0.001747488,0.001308841,0.009039865],"category_scores_gemma":[0.0149565,0.0007252634,0.0005804238,0.006154278,0.0006701496,0.00311055,0.001749509,0.001874266,0.01347827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008269642,"about_ca_system_score_gemma":0.002113175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00791532,"about_ca_topic_score_gemma":0.009380309,"domain_scores_codex":[0.997686,0.0004520876,0.0001803123,0.0003093578,0.001277383,0.00009492524],"domain_scores_gemma":[0.9917368,0.001458909,0.0005150621,0.001577681,0.004461377,0.000249994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001153711,0.00005740193,0.005884488,0.0006007939,0.00009768717,0.000133999,0.000169747,0.003421146,0.002817264,0.01082259,0.4127072,0.5631724],"study_design_scores_gemma":[0.00001506043,0.00002071216,0.004335497,0.0001764192,0.00003515132,0.0002594281,0.00004032997,0.002309327,0.00230619,0.002906733,0.9875473,0.00004786929],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01465584,0.03570244,0.7093484,0.01057192,0.01137652,0.0006837064,0.1228378,0.0306268,0.06419643],"genre_scores_gemma":[0.0309824,0.01896646,0.7685587,0.00298663,0.002212301,0.0006405489,0.1357891,0.01012785,0.02973592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009039865,"threshold_uncertainty_score":0.03024143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02154647382950215,"score_gpt":0.2587709758904167,"score_spread":0.2372245020609146,"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."}}