{"id":"W2995691767","doi":"10.11575/prism/36411","title":"Remote Sensing of Forest Fire Danger Forecasting","year":2019,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science Research and Technology; International Center for Agricultural Research in the Dry Areas; National Aeronautics and Space Administration","keywords":"Remote sensing; Environmental science; Geography; Environmental resource management; Forestry; Meteorology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001297245,0.0001572618,0.0003720371,0.0001325192,0.0001154068,0.00000937483,0.0001752154,0.0002518907,0.0001858417],"category_scores_gemma":[0.00003411786,0.0001664021,0.0001625465,0.0001270674,0.00005114844,0.00009711745,0.00000983348,0.000231107,0.00003500125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005143362,"about_ca_system_score_gemma":0.00008152024,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03608929,"about_ca_topic_score_gemma":0.004170818,"domain_scores_codex":[0.9990957,0.00004717172,0.000157495,0.0002401229,0.0002643089,0.0001952301],"domain_scores_gemma":[0.9991487,0.0001108119,0.0003546844,0.0002279292,0.00008725543,0.00007058377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001208904,0.000003391671,0.002772509,0.000221738,0.00004075216,0.0000413136,0.001692889,0.00002796359,0.00002126948,0.000001450623,0.0002361827,0.9948196],"study_design_scores_gemma":[0.000342205,0.00009906753,0.1112326,0.0005036261,0.0001255921,0.00001058078,0.000589504,0.8828954,0.00005504557,0.0001682796,0.003728372,0.0002497583],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9299548,0.000355443,0.003807443,0.00002201936,0.0004447816,0.0001396224,0.000003073196,0.00002204127,0.06525084],"genre_scores_gemma":[0.8953239,0.0003959957,0.07045796,0.00002399421,0.00005532154,5.51197e-10,0.002090801,0.0000185103,0.03163348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9945699,"threshold_uncertainty_score":0.9703295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464691925307718,"score_gpt":0.1858041220470244,"score_spread":0.1711572027939472,"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."}}