{"id":"W4387708418","doi":"10.21203/rs.3.rs-3428569/v1","title":"Improving wood carbon fractions for multiscale forest carbon estimation","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Forest ecology and management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Northern Research Station; University of Toronto Scarborough; U.S. Forest Service; University of Toronto; U.S. Department of Agriculture","keywords":"Carbon stock; Biome; Greenhouse gas; Environmental science; Biomass (ecology); Stock (firearms); Climate change; Estimation; Carbon accounting; Forestry; Agroforestry; Geography; Ecology; Biology; Ecosystem; Engineering","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":[0.0009316177,0.001034724,0.0008571661,0.001033016,0.000272442,0.0007285876,0.0005512286,0.0008473009,0.002538863],"category_scores_gemma":[0.007332726,0.0004585163,0.0006101566,0.0006321941,0.0003398387,0.0015324,0.0009429573,0.0008225184,0.000620671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002464767,"about_ca_system_score_gemma":0.0003952429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003961258,"about_ca_topic_score_gemma":0.004585818,"domain_scores_codex":[0.9997422,0.00007419199,0.00001589161,0.00008754009,0.00005136023,0.00002884886],"domain_scores_gemma":[0.9981886,0.001260628,0.00008204972,0.0002803296,0.0001347952,0.00005358041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003353933,0.0001337605,0.007969108,0.0002206023,0.0002474047,0.0001051509,0.0001060365,0.4835308,0.03934389,0.0116609,0.002672765,0.4536742],"study_design_scores_gemma":[0.000006979712,0.00001427724,0.001123693,0.00000645159,0.00001871093,0.00001970199,0.00000690667,0.9859955,0.004471246,0.007868694,0.0004614285,0.00000642808],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04768037,0.0002919179,0.9502339,0.0000957104,0.0000434777,0.00001707463,0.0002690887,0.0007294865,0.0006389105],"genre_scores_gemma":[0.5062407,0.000359382,0.4900273,0.00008153127,0.0001200643,0.0000694359,0.0008827684,0.0005473404,0.001671355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003961258,"threshold_uncertainty_score":0.008493304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04950271185886203,"score_gpt":0.3612080421311836,"score_spread":0.3117053302723216,"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."}}