{"id":"W4288789883","doi":"10.1504/ijgw.2022.124622","title":"Predictive modelling of boreal forest resources in regulation of the carbon cycle and mitigation of the global warming","year":2022,"lang":"en","type":"article","venue":"International Journal of Global Warming","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Taiga; Greenhouse gas; Global warming; Carbon cycle; Boreal; Ecosystem; Climate change; Balance of nature; Forest ecology; Primary production; Climatology; Atmospheric sciences; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003345959,0.00006989839,0.0001257918,0.000006855462,0.0000501653,0.000004961479,0.0003853893,0.00002636576,0.00001225749],"category_scores_gemma":[0.00003722855,0.00005044558,0.00008293229,0.0001779105,0.0002107019,0.0001160994,0.0003894567,0.0001081942,3.690101e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007166195,"about_ca_system_score_gemma":0.00001924908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0014767,"about_ca_topic_score_gemma":0.0001246585,"domain_scores_codex":[0.9984543,0.0000901459,0.000478951,0.00009296947,0.0008022639,0.00008139871],"domain_scores_gemma":[0.9990956,0.0000363982,0.0007385201,0.00008501775,0.00002176269,0.00002274459],"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":[0.00005319073,0.00002715705,0.4771123,0.000001664281,0.00001242687,7.390839e-7,0.0003125765,0.5211989,0.0002503644,0.0001091015,0.000001154532,0.0009204759],"study_design_scores_gemma":[0.0002584049,0.0000440062,0.6650815,0.00004640517,0.00001544866,0.00003763956,0.0007082015,0.3296834,0.0001940277,0.00387965,0.00001972881,0.00003160025],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964808,0.00005248564,0.001914651,0.0001960233,0.0001982988,0.00007898434,0.00002010917,0.000001077912,0.001057627],"genre_scores_gemma":[0.9985073,0.00001094267,0.001427687,0.00002080634,0.00001945869,0.000001053926,7.322922e-7,0.000003407044,0.000008646954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1915155,"threshold_uncertainty_score":0.2232338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004779579826139333,"score_gpt":0.2026720350568346,"score_spread":0.1978924552306952,"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."}}