{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001386566,0.0001475042,0.0002629501,0.00003401193,0.0003659933,0.00002215257,0.000118069,0.00008054655,0.0002364513],"category_scores_gemma":[0.0001346399,0.0001283097,0.00004202712,0.00006347751,0.0001072536,0.0001814339,0.0002087332,0.0001273047,0.00004549395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000234551,"about_ca_system_score_gemma":0.00006753083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001230015,"about_ca_topic_score_gemma":0.0001118907,"domain_scores_codex":[0.9980305,0.0002655434,0.0004042547,0.0003237423,0.0002243103,0.0007516582],"domain_scores_gemma":[0.9985212,0.00008412426,0.0001892135,0.0002600892,0.000009236052,0.0009361112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008905584,0.0003532125,0.8447037,0.00008296412,0.00000921935,0.000002781328,0.001451406,0.0001641804,0.1478785,0.0001406267,0.001319582,0.003804782],"study_design_scores_gemma":[0.0006612014,0.0007733403,0.9800022,0.000009400167,0.00002444969,0.00000187018,0.0001251697,0.0001399044,0.01636217,0.000424282,0.001314569,0.0001614432],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908959,0.00003959183,0.001722836,0.005817202,0.0002153803,0.001089207,0.00003727425,0.00004480857,0.0001377753],"genre_scores_gemma":[0.9800508,0.00002161379,0.0163683,0.003107819,0.00008174792,0.0001043607,0.00001652491,0.00001853166,0.0002303034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1352985,"threshold_uncertainty_score":0.5232319,"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."}}