{"id":"W2887195454","doi":"10.1242/dmm.035873","title":"<i>Drosophila melanogaster</i> as a function-based high-throughput screening model for antinephrolithiasis agents in kidney stone patients","year":2018,"lang":"en","type":"article","venue":"Disease Models & Mechanisms","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Lawson Health Research Institute; Western University","funders":"Schulich School of Medicine and Dentistry; National Institutes of Health; University of Glasgow; Kidney Foundation of Canada; Lawson Health Research Institute","keywords":"Drosophila melanogaster; Drosophila (subgenus); Throughput; Kidney stones; Function (biology); Biology; Computational biology; Computer science; Medicine; Evolutionary biology; Internal medicine; Genetics; Gene; Operating system","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":[0.0003387457,0.0004906415,0.0003759423,0.0004098026,0.0003256369,0.0004406575,0.0005299806,0.0004439311,0.002294526],"category_scores_gemma":[0.0001362504,0.0001893878,0.0005660944,0.000257116,0.0002296201,0.0001719255,0.0003245875,0.0006919209,0.0009996197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005605411,"about_ca_system_score_gemma":0.0003008454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002515126,"about_ca_topic_score_gemma":0.003657623,"domain_scores_codex":[0.9997125,0.00005751172,0.00003536325,0.00007191798,0.00009135775,0.00003116079],"domain_scores_gemma":[0.9998509,0.00003448366,0.0000394245,0.00002934914,0.00002153855,0.00002428238],"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":[0.00009080837,0.00003178815,0.0003602959,0.00006777624,0.00001555435,0.00005779402,0.00001507765,0.0002479301,0.9964755,0.0003027399,0.0004762321,0.001858542],"study_design_scores_gemma":[0.0000407506,0.0005001253,0.006197297,0.00002150521,0.00004989651,0.000414245,0.00003957843,0.005335968,0.9677702,0.000147242,0.01945566,0.00002755694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8953285,0.003625836,0.05651887,0.001047415,0.0002297185,0.0008962428,0.02513998,0.002543495,0.01466997],"genre_scores_gemma":[0.9046382,0.002053979,0.06005602,0.0005530105,0.00002325801,0.0008130585,0.01280761,0.0003419888,0.01871279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002515126,"threshold_uncertainty_score":0.007676005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03667904337769636,"score_gpt":0.287788995979653,"score_spread":0.2511099526019567,"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."}}