{"id":"W3185639651","doi":"10.1088/1755-1315/818/1/012020","title":"Role of Forests of the Volga River Basin in the Mitigation of Climate Fluctuations and of Forthcoming global Warming (Predictive Empirical-Statistical Modeling)","year":2021,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Global warming; Boreal; Environmental science; Climate change; Taiga; Greenhouse gas; Arid; Ecology; Structural basin; Ecosystem; Climatology; Physical geography; Geography; Forestry","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":[0.0004531021,0.0002147521,0.0002428447,0.0004608661,0.0004159059,0.0009447591,0.0004006405,0.0002935776,0.000611911],"category_scores_gemma":[0.001054924,0.0001329729,0.0004086412,0.000375707,0.0005561978,0.0004505303,0.000366414,0.0002473175,0.00003639976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006666153,"about_ca_system_score_gemma":0.0008842848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05236256,"about_ca_topic_score_gemma":0.04096109,"domain_scores_codex":[0.9998868,0.00005800658,0.000003637931,0.0000213494,0.000009767946,0.00002041246],"domain_scores_gemma":[0.9997318,0.0001666276,0.00003911318,0.00001806795,0.00002259043,0.00002176804],"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":[0.00002240258,0.00003233404,0.02356742,0.00002133795,0.00003655958,0.00006048934,0.00002683924,0.9661258,0.0003897826,0.005601984,0.0001720782,0.003942934],"study_design_scores_gemma":[0.000005024242,0.00001164693,0.009100088,0.000006636174,0.00001361637,0.00001729138,0.00004429615,0.9867173,0.0001113082,0.003719842,0.0002480637,0.000004889644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980342,0.0003809053,0.01509521,0.00037448,0.00001689697,0.00001375767,0.0002078814,0.0000824503,0.003486393],"genre_scores_gemma":[0.9988617,0.00007863681,0.0007626289,0.000009584045,0.000005778869,0.000005819197,0.00003986511,0.000004067245,0.000231983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05236256,"threshold_uncertainty_score":0.1041155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008059531958443639,"score_gpt":0.2193378068712269,"score_spread":0.2112782749127833,"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."}}