{"id":"W4281730041","doi":"10.1038/s41597-022-01396-1","title":"A global database of woody tissue carbon concentrations","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Centre for Global Change Science, University of Toronto; U.S. Forest Service; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Northern Research Station; University of Toronto; University of Toronto Scarborough; Impact Fund; U.S. Department of Agriculture","keywords":"Woody plant; Biome; Database; Environmental science; Coarse woody debris; Biology; Ecology; Ecosystem; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001464796,0.0009716715,0.0009211382,0.01704888,0.0003644372,0.001590534,0.001181627,0.0006835414,0.005013024],"category_scores_gemma":[0.004746596,0.0004191851,0.0004841347,0.02253809,0.000268657,0.001807877,0.0009837417,0.0007036767,0.005148213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005582152,"about_ca_system_score_gemma":0.001565707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007468313,"about_ca_topic_score_gemma":0.005350362,"domain_scores_codex":[0.9986848,0.0001062834,0.0003054614,0.0004667702,0.0003433596,0.00009332898],"domain_scores_gemma":[0.9915199,0.001499497,0.002250707,0.001349706,0.002885014,0.0004952585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001052332,0.0002244208,0.3829992,0.008205193,0.001052196,0.0007160055,0.0007370971,0.01127244,0.02478222,0.007367555,0.2150504,0.346541],"study_design_scores_gemma":[0.0001073185,0.0001178777,0.3747663,0.0007723446,0.0003291451,0.0007221025,0.0002732055,0.003630754,0.009795951,0.002511505,0.60681,0.000163558],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0304495,0.002426606,0.004589336,0.00005542049,0.00003371652,0.00006408371,0.9572937,0.0009459242,0.004141659],"genre_scores_gemma":[0.05013526,0.001335726,0.01009005,0.00006025495,0.00004770556,0.0002140526,0.9371107,0.0001916925,0.0008145652],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01704888,"threshold_uncertainty_score":0.01677024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05071179355147193,"score_gpt":0.2554810614561086,"score_spread":0.2047692679046367,"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."}}