{"id":"W4243635478","doi":"10.5194/gmd-2016-237","title":"The Fire Modeling Intercomparison Project (FireMIP), phase 1:Experimental and analytical protocols","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Bundesministerium für Bildung und Forschung; National Natural Science Foundation of China; National Science Foundation","keywords":"Benchmarking; Coupled model intercomparison project; Vegetation (pathology); Computer science; Component (thermodynamics); Set (abstract data type); Environmental science; Greenhouse gas; Variety (cybernetics); Systems engineering; Climate model; Meteorology; Environmental resource management; Climate change; Ecology; Engineering; Geography; Artificial intelligence","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":[0.02340345,0.001389955,0.0007311309,0.001064874,0.001965519,0.001599441,0.003173057,0.001436005,0.01695484],"category_scores_gemma":[0.02236458,0.0009373678,0.0007762963,0.001602173,0.001123275,0.001421376,0.002956358,0.002631242,0.005673498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294467,"about_ca_system_score_gemma":0.004988516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003061499,"about_ca_topic_score_gemma":0.002485709,"domain_scores_codex":[0.9933142,0.003117125,0.0005355048,0.0008399214,0.001832621,0.000360567],"domain_scores_gemma":[0.9907141,0.001545094,0.0004429321,0.004575153,0.002273771,0.0004489876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.007937977,0.00742181,0.02251536,0.002928307,0.0008331075,0.0007787338,0.002434781,0.132757,0.1558537,0.1291257,0.2778481,0.2595654],"study_design_scores_gemma":[0.003064079,0.003642257,0.01458765,0.0005424545,0.0002392092,0.0003651473,0.0005568278,0.1181995,0.2260454,0.05966016,0.5727436,0.0003538521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"protocol","genre_scores_codex":[0.1187513,0.0007696703,0.7004702,0.001233033,0.00127416,0.04708131,0.08636479,0.01087143,0.0331841],"genre_scores_gemma":[0.1494687,0.0007738489,0.6248574,0.0005849674,0.0002291046,0.1348877,0.07590788,0.004301804,0.008988511],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.02340345,"threshold_uncertainty_score":0.1237708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04486783222656015,"score_gpt":0.3608524250046651,"score_spread":0.3159845927781049,"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."}}