{"id":"W4200298400","doi":"10.1111/eea.13131","title":"Early instar mortality of a forest pest caterpillar: which mortality sources increase during an outbreak crash?","year":2021,"lang":"en","type":"article","venue":"Entomologia Experimentalis et Applicata","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Forêts, de la Faune et des Parcs","keywords":"Outbreak; Biology; PEST analysis; Ecology; Population; Population density; Predation; Demography; Botany","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005188749,0.0003646568,0.0004256088,0.00005749374,0.0002278133,0.00006568163,0.0007466025,0.0001991494,0.001934783],"category_scores_gemma":[0.00004739069,0.00036046,0.000105137,0.0004813119,0.0005045008,0.0004715535,0.001492815,0.0002252863,0.0001893103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002198999,"about_ca_system_score_gemma":0.00003740146,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01585446,"about_ca_topic_score_gemma":0.01475477,"domain_scores_codex":[0.9971433,0.0002709229,0.0005699058,0.0009644373,0.0004906051,0.0005607934],"domain_scores_gemma":[0.9980505,0.00003998469,0.0002596014,0.001353815,0.0000300951,0.0002660332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004965541,0.001094538,0.8529271,0.00003641161,0.0001137595,0.0001218109,0.001547368,0.0002325531,0.1417219,0.001860038,0.0002525422,0.0000422952],"study_design_scores_gemma":[0.000546195,0.000126596,0.7068768,0.000007915311,0.00004905339,0.00003354685,0.0006078543,0.00004352089,0.2907493,0.0005581064,0.0000998075,0.0003012884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918822,0.00008399502,0.00007049157,0.00006430108,0.0001024839,0.000577443,0.0001065769,0.0001396645,0.006972835],"genre_scores_gemma":[0.9981015,0.00003889417,0.0005110645,0.0003394648,0.00003520273,0.000346897,0.0003757812,0.00003538375,0.000215789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1490274,"threshold_uncertainty_score":0.9998847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922252510575681,"score_gpt":0.2868250702676962,"score_spread":0.2676025451619393,"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."}}