{"id":"W2981420946","doi":"10.4018/978-1-7998-1867-0.ch011","title":"Classification of Territory on Forest Fire Danger Level Using GIS and Remote Sensing","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in environmental engineering and green technologies book series","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vegetation (pathology); Remote sensing; Environmental science; Forest cover; Forestry; Vegetation cover; Land cover; Cover (algebra); Quarter (Canadian coin); Geography; Land use; Engineering; Civil engineering; Ecology","routes":{"ca_aff":false,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008378037,0.0003742343,0.000399839,0.0001171216,0.00005055049,0.00001088872,0.0001552443,0.0003512556,0.00001418588],"category_scores_gemma":[0.00001575858,0.0003792987,0.00004089021,0.00002620726,0.000559944,0.0005069672,0.0003117792,0.0003348943,0.00001089573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002962532,"about_ca_system_score_gemma":0.000003089566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001441947,"about_ca_topic_score_gemma":0.0001294093,"domain_scores_codex":[0.9987476,0.000007908618,0.0003098036,0.0004934599,0.0002155898,0.0002256682],"domain_scores_gemma":[0.9993042,0.00007338496,0.0001977007,0.0003958991,0.000001093196,0.00002768576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006193565,0.00001844728,0.004371549,0.0006435435,0.00004568764,0.00004767663,0.0001753151,0.006381734,0.02132425,0.0007920407,0.00001683663,0.966121],"study_design_scores_gemma":[0.001712339,0.001951787,0.04617302,0.006911505,0.0001900161,0.0005131533,0.0009291195,0.4091219,0.01084147,0.005985847,0.5114446,0.004225207],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9173545,0.04890861,0.001755439,0.0003400268,0.0009464895,0.002589212,0.0003927292,0.0009078597,0.02680508],"genre_scores_gemma":[0.9441674,0.02024464,0.01230495,0.00002928428,0.00004673141,0.000005482675,0.00003981078,0.0001767325,0.02298498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9618958,"threshold_uncertainty_score":0.9998659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00878000014959953,"score_gpt":0.1888714295313516,"score_spread":0.1800914293817521,"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."}}