{"id":"W2792792407","doi":"10.1534/g3.118.200257","title":"Comparative Transcriptomics Among Four White Pine Species","year":2018,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"U.S. Forest Service; U.S. Department of Agriculture; Office of Science; Rocky Mountain Research Station; European Molecular Biology Laboratory; U.S. Department of Energy","keywords":"Biology; Abiotic component; Mountain pine beetle; Ecology; Taiga; Botany; Transcriptome; Gene; Genetics","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":["insufficient_payload"],"category_scores_codex":[0.00013991,0.0002179664,0.0002243327,0.00005224088,0.0002628351,0.0000352518,0.0004049345,0.00009190479,0.005226445],"category_scores_gemma":[0.000001112456,0.0002109321,0.00007369963,0.0002486499,0.0008778871,0.00007801686,0.0002436684,0.00008737663,0.001646738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001029679,"about_ca_system_score_gemma":0.00001446644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003118032,"about_ca_topic_score_gemma":0.004303728,"domain_scores_codex":[0.9986762,0.00005370463,0.0002497555,0.0003825362,0.000201193,0.0004365537],"domain_scores_gemma":[0.9993709,0.00001771269,0.00008591927,0.0003887067,0.00001994229,0.0001167985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000876378,0.0002306874,0.7861688,0.00003193701,0.0002055409,0.00002772008,0.00852197,0.01231945,0.03266059,0.0005412473,0.02753585,0.1316686],"study_design_scores_gemma":[0.0003208301,0.0003062109,0.4903666,0.000003145975,0.0000427896,0.000007119775,0.0003192417,0.001757749,0.00427801,0.0004974576,0.5017992,0.0003016756],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9194433,0.01008163,0.003148955,0.0001389503,0.0007649286,0.0004537388,0.0000167221,0.00007369821,0.06587804],"genre_scores_gemma":[0.974722,0.007822897,0.005051778,0.000495603,0.0002519427,0.00002609676,0.00001323573,0.00002100205,0.01159547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4742633,"threshold_uncertainty_score":0.9991306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02590108432497662,"score_gpt":0.2295199062618257,"score_spread":0.2036188219368491,"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."}}