{"id":"W3099919314","doi":"10.3390/f11111215","title":"Integrating Neighborhood Effect and Supervised Machine Learning Techniques to Model and Simulate Forest Insect Outbreaks in British Columbia, Canada","year":2020,"lang":"en","type":"article","venue":"Forests","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Planbureau voor de Leefomgeving; Natural Sciences and Engineering Research Council of Canada; Seventh Framework Programme; Université de Montréal; Ministry of Forests, Lands and Natural Resource Operations","keywords":"Mountain pine beetle; Random forest; Land cover; Elevation (ballistics); Environmental science; Forest cover; Deforestation (computer science); Christian ministry; Infestation; Generalized linear model; Land use; Geography; Computer science; Ecology; Forestry; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001859564,0.0001235194,0.0002065092,0.00002008771,0.0001513238,0.00009467801,0.0001291316,0.00006628452,0.00009197093],"category_scores_gemma":[0.0001815876,0.0001650654,0.00001747614,0.0001778787,0.00006361823,0.0001329585,0.0003735787,0.0002380743,0.000004369785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110789,"about_ca_system_score_gemma":0.00002149178,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.834189,"about_ca_topic_score_gemma":0.9992848,"domain_scores_codex":[0.9989632,0.00006211425,0.0001859018,0.0003613487,0.0001341268,0.0002932635],"domain_scores_gemma":[0.9996197,0.00007494223,0.00003685127,0.00008657604,0.00000415526,0.0001778294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001942981,0.00001108292,0.9713975,0.00003003198,0.000006669946,0.0000703538,0.0002776285,0.01505469,0.0001956084,0.00001270104,0.000452388,0.01247194],"study_design_scores_gemma":[0.000363358,0.0003375949,0.713704,0.00003125186,0.000008586372,0.000008267238,0.00002082331,0.2846574,0.00002539129,0.0002807595,0.0003999004,0.0001626321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979887,0.00004473978,0.0003687361,0.0002077743,0.00002166712,0.000557164,0.00001012329,0.00005920138,0.0007418672],"genre_scores_gemma":[0.9978402,0.0000121211,0.0006501238,0.001263416,0.0000118557,0.00005787114,0.00001290556,0.00001947147,0.0001320259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2696027,"threshold_uncertainty_score":0.673117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005754911066858195,"score_gpt":0.1918792186609837,"score_spread":0.1861243075941255,"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."}}