{"id":"W4402504575","doi":"10.1139/cjfr-2024-0135","title":"Assessment of a probabilistic supervised machine learning method to estimate biomass expansion and conversion factors: a case study on cedar and pine trees","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biomass (ecology); Probabilistic logic; Forestry; Mathematics; Machine learning; Environmental science; Artificial intelligence; Statistics; Computer science; Biology; Ecology; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.005803641,0.0007332534,0.0006312443,0.0008329243,0.0004094546,0.0007615322,0.0008757067,0.001226541,0.0005040543],"category_scores_gemma":[0.007543242,0.0002493,0.0005758524,0.0005460781,0.0003611796,0.0007578611,0.0005674849,0.0007121008,0.0001224989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007651345,"about_ca_system_score_gemma":0.0009011935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01005309,"about_ca_topic_score_gemma":0.008083049,"domain_scores_codex":[0.9986046,0.000793813,0.00007933971,0.0002249149,0.000236489,0.00006089078],"domain_scores_gemma":[0.9947395,0.003959831,0.0002630389,0.0002354109,0.0007346449,0.00006765081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002551883,0.0003225559,0.01652568,0.0001195114,0.00009340308,0.0002573937,0.0001174751,0.8758508,0.002565733,0.00156767,0.0004623955,0.1018622],"study_design_scores_gemma":[0.000004722054,0.0000397623,0.001164787,0.000003337038,0.000005177612,0.00002226261,0.00001786485,0.9979545,0.0005008484,0.000214008,0.00006812021,0.000004632047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6032396,0.0003640856,0.392623,0.0004481897,0.00002667026,0.0001847721,0.0002114997,0.000536109,0.002366013],"genre_scores_gemma":[0.918714,0.0000827616,0.08039036,0.00004635727,0.00001274498,0.00007842798,0.0001613405,0.00002005304,0.0004939256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01005309,"threshold_uncertainty_score":0.03069299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04753780206504272,"score_gpt":0.3774931069404794,"score_spread":0.3299553048754367,"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."}}