{"id":"W2083700258","doi":"10.1016/j.tox.2012.10.014","title":"Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure","year":2012,"lang":"en","type":"article","venue":"Toxicology","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Profiling (computer programming); Gene expression profiling; Human health; Risk assessment; Carbon black; Disease; Carbon Nanoparticles; Computational biology; Gene expression; Medicine; Environmental health; Gene; Nanoparticle; Chemistry; Biology; Nanotechnology; Genetics; Computer science; Internal medicine; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009539257,0.0005121671,0.0004631935,0.0009693704,0.0002058333,0.0006918144,0.0002420807,0.0006807061,0.00104165],"category_scores_gemma":[0.000778224,0.0001527708,0.0003773926,0.0007056005,0.0003433578,0.0003812347,0.0002774069,0.0006055962,0.0003094873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004329438,"about_ca_system_score_gemma":0.0005554103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009905635,"about_ca_topic_score_gemma":0.001489787,"domain_scores_codex":[0.9996365,0.000100191,0.00002271482,0.00008797312,0.0001176176,0.00003493207],"domain_scores_gemma":[0.9995339,0.0001559423,0.0001243714,0.00004509128,0.0001164519,0.00002425654],"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":[0.0003511318,0.0001113896,0.03017138,0.0004125787,0.00005857707,0.0001607683,0.0001189905,0.001644071,0.9359806,0.0006623844,0.0003186718,0.03000949],"study_design_scores_gemma":[0.0000307699,0.001506275,0.2449533,0.0002210138,0.0003255429,0.0007085741,0.0007922534,0.01971357,0.7067019,0.005321328,0.01965683,0.00006868728],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8264149,0.0131492,0.1413585,0.002117357,0.0001995219,0.0005318682,0.00770185,0.0005313116,0.007995511],"genre_scores_gemma":[0.91888,0.008365132,0.0657342,0.000762245,0.00007522163,0.0004289493,0.00270236,0.00005463271,0.002997185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00104165,"threshold_uncertainty_score":0.005044937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05298518861127503,"score_gpt":0.3978727562056825,"score_spread":0.3448875675944074,"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."}}