{"id":"W2995577867","doi":"10.1111/eva.12902","title":"Forest genomics: Advancing climate adaptation, forest health, productivity, and conservation","year":2019,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"","keywords":"Threatened species; Biology; Biodiversity; Deforestation (computer science); Climate change; Productivity; Ecosystem services; Ecology; Forest ecology; Endangered species; Agroforestry; Environmental resource management; Adaptation (eye); Habitat; Ecosystem","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002708347,0.00009853013,0.00009926737,0.00004163172,0.0004025022,0.00001187616,0.0001109948,0.00004027115,0.0002918554],"category_scores_gemma":[0.00001243192,0.0001087037,0.00002100359,0.000210303,0.0001420979,0.0003368292,0.0001708421,0.00008059893,0.001105974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002696209,"about_ca_system_score_gemma":0.00003585074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003578622,"about_ca_topic_score_gemma":0.004358933,"domain_scores_codex":[0.9990347,0.00003595331,0.0002041727,0.0003558701,0.0001115662,0.0002577084],"domain_scores_gemma":[0.9994619,0.00005245096,0.0001241915,0.0002764391,0.00001414574,0.00007088108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002050781,0.0001546191,0.8457938,0.00005648368,0.00001287556,3.201231e-7,0.0002592602,0.05226842,0.0005373366,0.08678814,0.01005824,0.004049974],"study_design_scores_gemma":[0.0001494294,0.00004922753,0.8177047,0.000004538138,0.000006003263,0.00001174089,0.00009799494,0.01624845,0.000003876118,0.01565263,0.1499614,0.0001100007],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544547,0.000279492,0.02802913,0.008280341,0.0001467588,0.002745206,0.00003039323,0.0001438695,0.005890096],"genre_scores_gemma":[0.9891897,0.0001319439,0.008617071,0.0006405507,0.00004292369,0.0005722707,0.0001261619,0.00001227043,0.0006671248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1399032,"threshold_uncertainty_score":0.9996718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007473994075895873,"score_gpt":0.2202539203178925,"score_spread":0.2127799262419966,"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."}}