{"id":"W7053235900","doi":"","title":"An Uncertainty Analysis for Carbon Quantification from Above-Ground Tree Biomass: Predicting Uncertainties in Allometric Models in Canada and Sweden","year":2023,"lang":"en","type":"dissertation","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Peptidase Inhibition and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Uncertainty analysis; Climate change; Biomass (ecology); Uncertainty quantification; Climate change mitigation; Carbon sequestration; Reliability (semiconductor); Measurement uncertainty","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":[0.003688584,0.0008684207,0.0005766703,0.00249101,0.001545145,0.002307598,0.001501356,0.0007543643,0.0004490592],"category_scores_gemma":[0.008590772,0.0004390355,0.001312289,0.002711761,0.0008333943,0.000891249,0.001103019,0.0008866443,0.00005937149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01337869,"about_ca_system_score_gemma":0.01410171,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8718,"about_ca_topic_score_gemma":0.7914488,"domain_scores_codex":[0.9990632,0.0002145663,0.00004658426,0.0001510348,0.00037906,0.0001455248],"domain_scores_gemma":[0.9962727,0.002181259,0.0002083786,0.00009780926,0.001141684,0.00009817082],"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":[0.00006598449,0.00003586556,0.02043808,0.00007914591,0.0001122545,0.0001500207,0.0001915814,0.9579927,0.0004564316,0.004409749,0.000450923,0.01561734],"study_design_scores_gemma":[0.000005266306,0.00001356088,0.008538865,0.00003831994,0.00004218314,0.00002745582,0.0001836113,0.9878033,0.0007731065,0.00168064,0.0008601893,0.00003351156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8984127,0.001684655,0.08926751,0.0005215752,0.00002854325,0.0001509654,0.001599247,0.0002691638,0.008065525],"genre_scores_gemma":[0.9688538,0.0006436252,0.02834241,0.00004373588,0.000008345857,0.00006950216,0.0009010587,0.00005428725,0.001083367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1282,"threshold_uncertainty_score":0.25791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03841365266832427,"score_gpt":0.268193398094947,"score_spread":0.2297797454266227,"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."}}