{"id":"W2438588714","doi":"","title":"Increasing throughput in lead optimization in vivo toxicity screens.","year":2002,"lang":"en","type":"article","venue":"PubMed","topic":"Animal testing and alternatives","field":"Veterinary","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Greenfield Research (Canada)","funders":"","keywords":"Lead (geology); Computer science; Throughput; Selection (genetic algorithm); Risk analysis (engineering); Turnaround time; Biochemical engineering; Limited resources; Machine learning; Business; Engineering; Telecommunications; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003313583,0.0001007675,0.0001378794,0.0001133452,0.00003597334,0.0000297471,0.0001077548,0.00004817618,0.0001037443],"category_scores_gemma":[0.0004524202,0.0001027679,0.00002442523,0.0002515125,0.00003778676,0.0002232264,0.00005277462,0.0001359811,0.00001497553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008677645,"about_ca_system_score_gemma":0.000002612177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006428071,"about_ca_topic_score_gemma":0.00006927873,"domain_scores_codex":[0.99902,0.0001378202,0.000203077,0.0002247763,0.0001127672,0.0003015569],"domain_scores_gemma":[0.999644,0.0001294407,0.00005946257,0.0001119453,0.00001562599,0.00003951686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006934532,0.0009809558,0.7610182,0.00007960327,0.00003140436,0.0007009932,0.002263442,0.01453923,0.00211428,0.0004829494,0.0008306286,0.2162649],"study_design_scores_gemma":[0.001067519,0.0001208072,0.8257899,0.00008286678,0.000005204118,0.00009205864,0.0001881368,0.1713016,0.0005513201,0.0001754373,0.0002928472,0.0003322098],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962868,0.0001346154,0.0005636907,0.0001678018,0.0000465204,0.0002416205,0.000003919266,0.00008724298,0.03588657],"genre_scores_gemma":[0.9956335,0.00002901731,0.003598096,0.00011558,0.00008433091,0.0001208024,0.000001370506,0.00001551939,0.000401824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2159327,"threshold_uncertainty_score":0.4190753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1990000423328801,"score_gpt":0.3278989951934348,"score_spread":0.1288989528605547,"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."}}