{"id":"W3004878516","doi":"10.1071/wf19061","title":"Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide","year":2020,"lang":"en","type":"article","venue":"International Journal of Wildland Fire","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Ministry of Agriculture and Forestry","funders":"Bundesministerium für Bildung und Forschung; Deutscher Akademischer Austauschdienst; Bayerisches Staatsministerium für Umwelt und Verbraucherschutz; U.S. Forest Service; European Commission; Department of Environment, Land, Water and Planning, State Government of Victoria","keywords":"Moisture; Environmental science; Water content; Range (aeronautics); Context (archaeology); Calibration; Meteorology; Soil science; Materials science; Geography; Geology; Mathematics; Statistics; Geotechnical engineering; Composite material; Archaeology","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":[0.0009076903,0.0005045612,0.0005212869,0.00170535,0.0003387556,0.000593752,0.0005008326,0.0004002378,0.00115754],"category_scores_gemma":[0.00147493,0.0002138453,0.0003878468,0.001600394,0.0002827621,0.0005949822,0.0004145387,0.0002930114,0.0004123405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002757088,"about_ca_system_score_gemma":0.0001371612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0105678,"about_ca_topic_score_gemma":0.03285994,"domain_scores_codex":[0.9993621,0.0001136688,0.00003331449,0.0002721155,0.0001823739,0.00003645242],"domain_scores_gemma":[0.9990502,0.0002855484,0.0002581022,0.0001276319,0.0002300634,0.00004837997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002929796,0.0001164491,0.8973358,0.0001090718,0.0002795223,0.00007173565,0.0003958156,0.004930197,0.02225649,0.00007831225,0.0002645936,0.07386906],"study_design_scores_gemma":[0.000009195778,0.0001751444,0.9851403,0.00003283301,0.00006370303,0.0002090807,0.0002786524,0.00791853,0.00496752,0.0001454341,0.001035113,0.00002449417],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897596,0.000218051,0.007704307,0.000008692917,0.000008845225,0.00004981196,0.001123701,0.00007938012,0.001047623],"genre_scores_gemma":[0.9804432,0.0001630108,0.01674389,0.00002338303,0.00001189063,0.00006879082,0.001933468,0.00003688475,0.0005754931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0105678,"threshold_uncertainty_score":0.02101254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196636732359171,"score_gpt":0.2707641932474888,"score_spread":0.2587978259238971,"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."}}