{"id":"W1465231413","doi":"10.1007/978-3-319-19809-5_6","title":"Individual-Based Modeling: Mountain Pine Beetle Seasonal Biology in Response to Climate","year":2015,"lang":"en","type":"book-chapter","venue":"","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Mountain pine beetle; Dendroctonus; Climate change; Ecology; Geography; Population; Boreal; Range (aeronautics); Bark beetle; Abies balsamea; Taiga; Outbreak; Biology; Balsam; Bark (sound)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003880731,0.0004998423,0.0007207587,0.0001897829,0.0003187796,0.000843021,0.00175908,0.001345188,0.003603132],"category_scores_gemma":[0.001132909,0.0004733945,0.0006414152,0.0005584424,0.0003501973,0.0007610854,0.0005867212,0.001009911,0.0005977791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005604842,"about_ca_system_score_gemma":0.0006499673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03332861,"about_ca_topic_score_gemma":0.03249252,"domain_scores_codex":[0.9999192,0.00002871999,0.000003327921,0.000024003,0.00001497548,0.000009717761],"domain_scores_gemma":[0.9997035,0.0001920041,0.00002964341,0.00001990525,0.00002768678,0.00002732302],"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":[0.000009561695,0.00001465842,0.0005755044,0.00002463534,0.00004518472,0.00002165814,0.00002817928,0.9828731,0.0002814865,0.005079966,0.002548754,0.008497284],"study_design_scores_gemma":[0.000002404006,0.000002868008,0.0002871169,0.000005907233,0.000009020375,0.000008941356,0.000005114112,0.9941193,0.00004451423,0.004425544,0.001084265,0.00000516235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1277987,0.007852647,0.8174844,0.003505908,0.0006098016,0.00004135391,0.001988966,0.001553823,0.03916431],"genre_scores_gemma":[0.8659042,0.006260107,0.08374188,0.000873486,0.0007946857,0.0002269643,0.001341024,0.0006551289,0.04020261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03332861,"threshold_uncertainty_score":0.06626928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331380169527843,"score_gpt":0.2657206433150356,"score_spread":0.2325826263622513,"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."}}