{"id":"W2041339881","doi":"10.1089/zeb.2004.1.185","title":"Investigator Profile: An Interview with Zhiyuan Gong, Ph.D.","year":2004,"lang":"en","type":"article","venue":"Zebrafish","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biology; Computational biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000877095,0.0001602556,0.0003157964,0.00009879882,0.00007790061,0.00003157685,0.0001492148,0.000114696,0.0006244797],"category_scores_gemma":[0.008850629,0.0001058851,0.00004285574,0.0003391796,0.0002338392,0.0002094469,0.0000392627,0.0005228514,0.0003192365],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001963509,"about_ca_system_score_gemma":0.006737798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001565063,"about_ca_topic_score_gemma":0.00008295798,"domain_scores_codex":[0.9977524,0.00008491649,0.0002449518,0.0003000491,0.0006802399,0.0009374525],"domain_scores_gemma":[0.9887444,0.00003629178,0.0000479677,0.0004122345,0.0001463809,0.01061277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001437665,0.003340286,0.01690301,0.005599198,0.0002846894,0.003106377,0.004667745,0.000003395194,0.01121418,0.005530523,0.5378268,0.4100861],"study_design_scores_gemma":[0.0118348,0.01074276,0.03837384,0.003387641,0.0001012426,0.0006179217,0.0004840383,0.00006480116,0.0209119,0.00111043,0.9118765,0.0004941133],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8528319,0.0003839532,0.0003503142,0.1288442,0.0001541072,0.001262516,0.00001478786,0.0002650459,0.01589318],"genre_scores_gemma":[0.850525,0.0001309283,0.003771816,0.1428355,0.0006749764,0.00009599426,0.00009192015,0.0000657003,0.001808207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.409592,"threshold_uncertainty_score":0.9994982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1405434829202391,"score_gpt":0.3976542011275594,"score_spread":0.2571107182073203,"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."}}