{"id":"W1992948520","doi":"10.1016/j.aca.2012.03.001","title":"Recognition of chemical compounds in contaminated water using time-dependent multiple dose cellular responses","year":2012,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Health; University of Alberta","funders":"Alberta Health; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Toxicant; Chemistry; Cytotoxicity; Support vector machine; Lysis; Biological system; Cell; Identification (biology); Artificial intelligence; Toxicity; Biochemistry; Computer science; In vitro","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":[0.00009722354,0.0001962754,0.000332176,0.0001784911,0.00001661152,0.000009416558,0.0001851141,0.0002042463,0.00008024573],"category_scores_gemma":[0.0001924877,0.000171114,0.00006953217,0.0001917782,0.0001110953,0.0001900965,0.00008319473,0.0002785997,0.00004680757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001758913,"about_ca_system_score_gemma":0.000002830861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007242535,"about_ca_topic_score_gemma":0.000001247224,"domain_scores_codex":[0.9987842,0.00002772679,0.0003817197,0.0001757049,0.0001564161,0.0004742146],"domain_scores_gemma":[0.9993768,0.0001904472,0.00004310576,0.0002756102,0.00003799435,0.00007605027],"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":[0.00006682749,0.00009087907,0.0007930524,0.000031885,0.00003701937,0.000003533994,0.00009356977,0.00003177443,0.9986618,0.000002076094,0.00003398917,0.0001536662],"study_design_scores_gemma":[0.0004945383,0.00001236153,0.0001562628,0.00004406282,0.00004042959,0.000008820397,0.00004926663,0.02514124,0.9735795,0.0001600481,0.00008556339,0.0002279578],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990529,0.00001985521,0.00003402162,0.00004387663,0.00004226888,0.0001258977,0.00001975786,0.0002759121,0.0003854736],"genre_scores_gemma":[0.9985262,0.00001507252,0.001289384,0.00001144696,0.00003191927,0.00000519734,0.0000552694,0.000040167,0.0000253062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02510947,"threshold_uncertainty_score":0.6977827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02250947540551771,"score_gpt":0.2344201359951977,"score_spread":0.21191066058968,"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."}}