{"id":"W4318541027","doi":"10.5281/zenodo.7583611","title":"Mapping Global Live Woody Vegetation Biomass at Optimum Spatial Resolutions","year":2023,"lang":"fr","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biomass (ecology); Vegetation (pathology); Environmental science; Geography; Forestry; Physical geography; Cartography; Remote sensing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009008496,0.0003919521,0.0003248549,0.001339759,0.0002679979,0.0009684957,0.000530345,0.0004893424,0.0062506],"category_scores_gemma":[0.002011549,0.0003172885,0.0004879969,0.001665498,0.000136908,0.0009271968,0.0008901458,0.0003970263,0.003313407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002190961,"about_ca_system_score_gemma":0.0003625626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007511171,"about_ca_topic_score_gemma":0.02729537,"domain_scores_codex":[0.999662,0.00004320302,0.00001147848,0.00007350238,0.0001564963,0.00005332831],"domain_scores_gemma":[0.9993279,0.000125588,0.00003514275,0.0001062763,0.0003388435,0.00006631953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007591916,0.0001145375,0.1342176,0.0007215099,0.0004182367,0.0009632806,0.0006472305,0.08224817,0.07833296,0.006192333,0.1992019,0.4961829],"study_design_scores_gemma":[0.0001991039,0.0001457859,0.3891837,0.0003511195,0.0004012925,0.0008968711,0.002107792,0.2979557,0.04872549,0.01049668,0.2493368,0.000199601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5233179,0.006849634,0.2454155,0.00557193,0.001177772,0.0001674615,0.08747268,0.01988549,0.1101417],"genre_scores_gemma":[0.7420385,0.002855837,0.1540637,0.0005282774,0.000554842,0.0002206803,0.08105791,0.001688235,0.01699201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007511171,"threshold_uncertainty_score":0.02091032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06833992415737378,"score_gpt":0.2781149484201329,"score_spread":0.2097750242627591,"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."}}