{"id":"W2097689680","doi":"10.1128/aem.00873-10","title":"Metatranscriptomic Analysis of the Response of River Biofilms to Pharmaceutical Products, Using Anonymous DNA Microarrays","year":2010,"lang":"en","type":"article","venue":"Applied and Environmental Microbiology","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Saskatchewan; Biotechnology Research Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biofilm; Biology; DNA microarray; Gene; Transcriptional regulation; Transcription (linguistics); Microbiology; DNA; Aquatic ecosystem; Gene expression; Bacteria; Genetics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002614424,0.0002174278,0.0003790227,0.00008800724,0.0001550883,0.000003650202,0.0003341599,0.0001196194,0.0005681595],"category_scores_gemma":[0.000009191989,0.000170209,0.0001246251,0.0003403705,0.002530626,0.00003840027,0.0006521398,0.0001802455,0.00004606152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008286022,"about_ca_system_score_gemma":0.000003087505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008911975,"about_ca_topic_score_gemma":0.00001683805,"domain_scores_codex":[0.9987348,0.00008274904,0.0002917259,0.0004917458,0.0001035703,0.0002954207],"domain_scores_gemma":[0.9993597,0.00005974499,0.0001291266,0.0003726521,0.000001267842,0.00007746397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001624924,0.0001186833,0.1818864,0.000005356683,0.0002311082,6.018116e-7,0.0006379621,0.0001622816,0.8165257,0.000005877397,0.00007440472,0.0001891166],"study_design_scores_gemma":[0.0002522424,0.00003689949,0.3803146,0.000001303703,0.0004697996,0.000008817612,0.0001921946,0.00001582727,0.6167669,0.000003655664,0.001803111,0.0001346249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988173,0.00006053761,0.00001763001,0.0001259316,0.0001221992,0.0003830749,0.0003882458,0.000007374079,0.0000777484],"genre_scores_gemma":[0.9938135,0.00003570852,0.00574924,0.0003005686,0.000008066648,0.000005854387,0.00002097592,0.0000112295,0.00005482131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1997588,"threshold_uncertainty_score":0.9324198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01015120011027416,"score_gpt":0.2087580974896033,"score_spread":0.1986068973793291,"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."}}