{"id":"W4295180709","doi":"10.1111/gcb.16384","title":"Accuracy, realism and general applicability of European forest models","year":2022,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"European Regional Development Fund; Bundesministerium für Bildung und Forschung","keywords":"Eddy covariance; Environmental science; Random forest; Climate change; Forest ecology; Forest inventory; Ecology; Atmospheric sciences; Forest management; Ecosystem; Computer science; Agroforestry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000161613,0.00005606175,0.00007803752,0.000006962035,0.00006379189,0.00000252325,0.0001303266,0.00002214832,0.0000811778],"category_scores_gemma":[0.000003327081,0.00004992616,0.0000196438,0.00007822848,0.0001078956,0.00003807788,0.000514639,0.00004562846,0.000005552192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006992448,"about_ca_system_score_gemma":0.000001967778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001823721,"about_ca_topic_score_gemma":0.000171888,"domain_scores_codex":[0.9994513,0.000100163,0.00009855002,0.0001727178,0.00005381022,0.0001234348],"domain_scores_gemma":[0.9997748,0.00001008251,0.00004826452,0.000129257,0.000001960195,0.00003567753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003443352,0.00007509433,0.9394754,0.000005277099,0.00001038029,0.000006525126,0.0003125227,0.002690237,0.0007702786,0.02767854,0.0002799166,0.02866136],"study_design_scores_gemma":[0.0005862584,0.0003421608,0.7072805,0.000001994299,0.00002416103,0.0001296254,0.00006749752,0.1745283,0.000007861411,0.07096484,0.04572278,0.0003440487],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847762,0.00009101378,0.0003780438,0.0001204554,0.0000557158,0.0001301999,0.0005521572,0.00001492957,0.01388128],"genre_scores_gemma":[0.9993726,0.00004076517,0.0002402273,0.0001554885,0.0000263347,0.00002392314,0.0001144043,0.000002602337,0.0000236719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.232195,"threshold_uncertainty_score":0.2756933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02941970497142174,"score_gpt":0.2416054892923747,"score_spread":0.212185784320953,"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."}}