{"id":"W4213148573","doi":"10.5194/bg-2016-17","title":"The status and challenge of global fire modelling","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Seventh Framework Programme","keywords":"Underpinning; Environmental science; Fire regime; Benchmark (surveying); Variety (cybernetics); Vegetation (pathology); Climate model; Climate change; Empirical modelling; Environmental resource management; Biomass burning; Climatology; Computer science; Meteorology; Geography; Ecology; Ecosystem; Engineering; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005360514,0.0007302841,0.0009502739,0.0008666315,0.000529871,0.003965088,0.00167891,0.001790327,0.002166567],"category_scores_gemma":[0.01070892,0.0003279393,0.000922613,0.001396251,0.001954367,0.007109948,0.002091278,0.002756212,0.0005753608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001686923,"about_ca_system_score_gemma":0.002036767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009305005,"about_ca_topic_score_gemma":0.004193207,"domain_scores_codex":[0.9988355,0.0006537547,0.00006159976,0.0001447247,0.0002443246,0.00006015805],"domain_scores_gemma":[0.9959491,0.002640437,0.0001516293,0.0007130329,0.0004045397,0.0001412759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005468775,0.00007556019,0.003821419,0.0005314605,0.0001801626,0.00005187058,0.0002786803,0.3822256,0.0004153917,0.3915017,0.0211944,0.199669],"study_design_scores_gemma":[0.00002442399,0.00002391324,0.001077542,0.0004113009,0.00003533201,0.00003501652,0.0002409897,0.4026211,0.0002967308,0.5221783,0.0730179,0.00003745195],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214731,0.1399404,0.4232903,0.2084189,0.004925278,0.0001294318,0.003101462,0.002625727,0.09609554],"genre_scores_gemma":[0.7746728,0.1051912,0.1019315,0.003944544,0.003854793,0.0002029541,0.002370135,0.0006886793,0.007143452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009305005,"threshold_uncertainty_score":0.02834946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282329555399069,"score_gpt":0.224361960156635,"score_spread":0.2115386646026443,"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."}}