{"id":"W3171924875","doi":"10.1002/ece3.7921","title":"Predicting insect outbreaks using machine learning: A mountain pine beetle case study","year":2021,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Random forest; Machine learning; Computer science; Predictive modelling; Artificial intelligence; Mountain pine beetle; Regression; Statistics; Ecology; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002217831,0.000857613,0.0005329616,0.001028358,0.0007039027,0.0008813424,0.001062716,0.001225715,0.0008236266],"category_scores_gemma":[0.002574577,0.000226044,0.0008154177,0.0007998031,0.0005112918,0.0006904812,0.0004471042,0.001154784,0.0001952956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171366,"about_ca_system_score_gemma":0.0005861215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04512297,"about_ca_topic_score_gemma":0.05299006,"domain_scores_codex":[0.9994267,0.0002858103,0.00003160524,0.00009805465,0.00007942828,0.00007845],"domain_scores_gemma":[0.9974172,0.001752067,0.0001887152,0.0001830679,0.0002787493,0.0001802568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004093692,0.0009527418,0.1690732,0.000136409,0.0002701194,0.002475483,0.0001961029,0.7856929,0.001087069,0.00150473,0.008599556,0.02960214],"study_design_scores_gemma":[0.00005429521,0.0002168512,0.02384474,0.00002580766,0.00004580591,0.0002038819,0.0002687925,0.9722634,0.000913599,0.0009072408,0.001228765,0.00002681495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900542,0.000654491,0.005081693,0.001292668,0.00004216965,0.00005706217,0.0009320587,0.0002049121,0.001680706],"genre_scores_gemma":[0.9905695,0.0001878041,0.006896495,0.0001032081,0.00006143257,0.00002352862,0.001303311,0.0000146974,0.0008400485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04512297,"threshold_uncertainty_score":0.08972067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393774435485742,"score_gpt":0.2362046730804383,"score_spread":0.2222669287255809,"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."}}