{"id":"W3213708175","doi":"10.1111/rec.13591","title":"Carbon content and allometric models to estimate aboveground biomass for forest areas under restoration","year":2021,"lang":"en","type":"article","venue":"Restoration Ecology","topic":"Forest ecology and management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Tree allometry; Carbon sequestration; Environmental science; Carbon stock; Biomass (ecology); Allometry; Atlantic forest; Forestry; Forest inventory; Stock (firearms); Carbon fibers; Restoration ecology; Ecology; Climate change; Agroforestry; Forest management; Mathematics; Biology; Carbon dioxide; Geography; Biomass partitioning; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002964637,0.00013118,0.000172985,0.0001301986,0.0001826625,0.00004210455,0.00009000362,0.0001382013,0.00008422588],"category_scores_gemma":[0.0001412843,0.0001365976,0.0000300158,0.0003661529,0.00009462382,0.000254356,0.0001459282,0.00005808279,0.00004858725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003673839,"about_ca_system_score_gemma":0.00003518681,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000223684,"about_ca_topic_score_gemma":0.03866162,"domain_scores_codex":[0.998862,0.00007927157,0.00024627,0.0004042954,0.0001280152,0.0002801543],"domain_scores_gemma":[0.9994095,0.0001230019,0.000095561,0.0002119715,0.00004523218,0.0001147147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000351118,0.0006005837,0.312403,0.00008873875,0.0001435924,0.00007163008,0.0006939889,0.2781857,0.0139884,0.3787699,0.01269196,0.002011384],"study_design_scores_gemma":[0.0008796941,0.0004849318,0.8952355,0.000004995735,0.00004817473,0.00001685173,0.000131767,0.05369819,0.0003533112,0.04228978,0.006610499,0.0002463285],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9359121,0.00006175716,0.05795237,0.002965206,0.0004875743,0.0006940624,0.000005206503,0.00004416028,0.001877598],"genre_scores_gemma":[0.9940519,0.0000135474,0.002967364,0.0007294099,0.00003442701,0.0002490956,0.00006506209,0.00001457073,0.001874674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5828325,"threshold_uncertainty_score":0.9788803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04547536422049544,"score_gpt":0.2741480884846689,"score_spread":0.2286727242641735,"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."}}