{"id":"W2145785970","doi":"10.2174/138620712801619140","title":"Advances in Zebrafish High Content and High Throughput Technologies","year":2012,"lang":"en","type":"review","venue":"Combinatorial Chemistry & High Throughput Screening","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Zebrafish; Danio; Drug discovery; Computational biology; Biology; Pharmacogenomics; High-content screening; Drug development; Computer science; Bioinformatics; Drug; Gene; Genetics; Cell; Pharmacology","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007300343,0.001066876,0.002014724,0.0001324876,0.0002858665,0.000152669,0.001523291,0.001952558,0.00006410226],"category_scores_gemma":[0.001041182,0.0009925206,0.0003709349,0.0007632287,0.001064498,0.00006974368,0.001944387,0.001436732,0.00002473777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000209611,"about_ca_system_score_gemma":0.0003570327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001994192,"about_ca_topic_score_gemma":0.00001577161,"domain_scores_codex":[0.9947286,0.000193597,0.001207259,0.001743103,0.0007789958,0.001348464],"domain_scores_gemma":[0.9969388,0.0002693167,0.0006537127,0.001544353,0.0002243946,0.0003693684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001263366,0.0004916354,0.00006485754,0.008935424,0.0003978532,0.00004386439,0.00001960467,0.000001292488,0.004353553,0.007530162,0.001953435,0.976082],"study_design_scores_gemma":[0.001593954,0.0001607625,0.00002430069,0.002447193,0.0002809138,0.00005135407,0.00008574363,0.000001481638,0.008794728,0.003178746,0.9823074,0.001073406],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008544559,0.9948005,0.0008785671,0.0003182703,0.0008552145,0.001208104,0.0002512863,0.0002161923,0.0006174047],"genre_scores_gemma":[0.02873216,0.9644172,0.001814012,0.00003669254,0.00166533,0.0008330538,0.002127891,0.0001681201,0.0002056101],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.980354,"threshold_uncertainty_score":0.9993431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04294576305667806,"score_gpt":0.3236897361803796,"score_spread":0.2807439731237015,"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."}}