{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000380673,0.00009829433,0.0001244771,0.00003823218,0.0005578903,0.00001084002,0.00004413993,0.00009447125,0.000783317],"category_scores_gemma":[0.0001079801,0.000101113,0.0000190063,0.0001836216,0.000113627,0.0001247124,0.0002803318,0.0002062268,0.00005350403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002095808,"about_ca_system_score_gemma":0.00001717781,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00434894,"about_ca_topic_score_gemma":0.04233835,"domain_scores_codex":[0.9990301,0.0002277052,0.0001557246,0.0002915638,0.00006588602,0.0002290166],"domain_scores_gemma":[0.9997202,0.00005075451,0.00006257363,0.0001079617,0.000008643029,0.00004986831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001983594,0.0002514232,0.9909237,0.000005389943,0.00003007544,0.001522602,0.0006698868,0.005052886,0.001215009,0.0000798752,0.00005513882,0.000174157],"study_design_scores_gemma":[0.0006871901,0.000353468,0.9315886,0.000002318381,0.00006023511,0.002631405,0.001996115,0.06192778,0.00002453232,0.0002987364,0.0003162079,0.000113438],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982296,0.00008768392,0.0002670452,0.00005477469,0.0002184107,0.0002105631,0.00000184655,0.00004576521,0.0008843021],"genre_scores_gemma":[0.9988875,0.00000852124,0.00021243,0.00009857,0.0000342225,0.00001900146,0.00000786812,0.000007312475,0.0007245999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05933515,"threshold_uncertainty_score":0.9751365,"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."}}