{"id":"W2318100480","doi":"10.1093/neuonc/nov061.134","title":"PM-12 * USING A ZEBRAFISH PEDIATRIC BRAIN TUMOR MODEL FOR PRE-CLINICAL DRUG SCREENING","year":2015,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Zebrafish Biomedical Research Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Zebrafish; Drug; Medicine; Computational biology; Oncology; Pharmacology; Biology; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005056351,0.0008802434,0.0005428774,0.0006217933,0.0004692169,0.0004548008,0.0006954571,0.0008090814,0.004034673],"category_scores_gemma":[0.0002315952,0.0002970859,0.00059376,0.0002684872,0.0005077703,0.0004603031,0.0003489585,0.001326217,0.0009992636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001404418,"about_ca_system_score_gemma":0.00160456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009235417,"about_ca_topic_score_gemma":0.02088366,"domain_scores_codex":[0.9997391,0.00003264383,0.00001928285,0.00006110081,0.0001002826,0.00004762976],"domain_scores_gemma":[0.9998097,0.00003190208,0.0000453194,0.0000248915,0.00003529586,0.00005298334],"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":[0.0002927461,0.0001906196,0.0004525258,0.0001183682,0.0000218646,0.0002882885,0.00003828737,0.0007282163,0.9872953,0.0007122014,0.001397052,0.008464558],"study_design_scores_gemma":[0.0001791245,0.002912982,0.002148407,0.0000328616,0.00007738611,0.0006962711,0.00004110863,0.003037402,0.9730641,0.0001915551,0.01758958,0.00002924422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8526629,0.003476688,0.09554177,0.00291686,0.000434764,0.003816853,0.01280394,0.003878293,0.02446811],"genre_scores_gemma":[0.8135443,0.005551064,0.1346913,0.0007396709,0.00003734319,0.003896985,0.007545585,0.0005355783,0.03345827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009235417,"threshold_uncertainty_score":0.0183633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08179235148346989,"score_gpt":0.4067549200181884,"score_spread":0.3249625685347185,"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."}}