{"id":"W2000016354","doi":"10.1186/1471-2164-11-215","title":"Functional genomics of mountain pine beetle (Dendroctonus ponderosae) midguts and fat bodies","year":2010,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Center for Research Resources; U.S. Department of Agriculture; U.S. Forest Service; National Institutes of Health; National Science Foundation","keywords":"Biology; Expressed sequence tag; Gene; Transcriptome; Dendroctonus; DNA microarray; Complementary DNA; Functional genomics; Genetics; Contig; Microarray; cDNA library; Microarray analysis techniques; Gene expression; Computational biology; Genomics; Genome; Bark beetle; Botany","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":[0.0001688039,0.0001171117,0.0001283801,0.00003547467,0.0001220281,0.00001293732,0.000145375,0.00009105704,0.001207637],"category_scores_gemma":[0.00002176839,0.0001182201,0.00003803268,0.00004397583,0.0002940784,0.00008138177,0.0003102534,0.0001342392,0.0002126149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009214884,"about_ca_system_score_gemma":0.00001859473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001900107,"about_ca_topic_score_gemma":0.007545268,"domain_scores_codex":[0.999249,0.00001524201,0.0001819599,0.0002342544,0.00009586199,0.0002237027],"domain_scores_gemma":[0.9996038,0.00003916155,0.00008252713,0.0002066354,0.000006495584,0.00006134446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002006209,0.0002754844,0.3222875,0.00007805636,0.00007580251,0.0000081654,0.001154735,0.007054069,0.6435769,0.01354126,0.009571913,0.002175424],"study_design_scores_gemma":[0.0006689521,0.0001077218,0.9447769,0.000001709801,0.00003452509,0.00003099647,0.0002674387,0.00108246,0.007730389,0.006234321,0.03882495,0.0002396315],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960229,0.00003865154,0.0007264604,0.00006851819,0.0003459585,0.0001757146,0.00001457112,0.00001802781,0.002589174],"genre_scores_gemma":[0.9938349,0.00005881861,0.003887037,0.0001946161,0.00006293774,0.0000116412,0.00001540904,0.00001302533,0.00192158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6358466,"threshold_uncertainty_score":0.9997054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0128338350149919,"score_gpt":0.2068706276216682,"score_spread":0.1940367926066764,"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."}}