{"id":"W4241342873","doi":"10.3410/f.1022074.292795","title":"Faculty Opinions recommendation of Phagemid encoded small molecules for high throughput screening of chemical libraries.","year":2005,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Throughput; High-throughput screening; Computer science; Chemistry; Computational biology; Combinatorial chemistry; Information retrieval; Biology; Telecommunications; Biochemistry","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001235371,0.0004984295,0.0009211887,0.0002296446,0.0001204753,0.00007959918,0.001931979,0.001087045,0.0001261211],"category_scores_gemma":[0.008783295,0.0003430376,0.0007062866,0.0008175839,0.0007846817,0.0000379445,0.0009171769,0.0006134037,0.000006180294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003588537,"about_ca_system_score_gemma":0.0006874508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003651823,"about_ca_topic_score_gemma":0.000004181252,"domain_scores_codex":[0.9956281,0.0002206172,0.001894157,0.0006258244,0.001167435,0.0004638608],"domain_scores_gemma":[0.9922576,0.0001317729,0.001478894,0.001300038,0.004516683,0.0003150145],"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":[0.000058641,0.000337687,0.000001629109,0.007483467,0.000221257,6.940238e-8,0.00004715069,3.352112e-7,0.0003537257,0.0001424334,0.9818485,0.009505071],"study_design_scores_gemma":[0.001048682,0.0003374597,0.00007657693,0.003399859,0.0001446294,0.000009942821,0.00003061102,0.00004573187,0.009759208,0.00004142558,0.9847841,0.0003217464],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004421297,0.001529905,0.001982396,0.04663122,0.0003744868,0.001542567,0.9478641,0.00001298405,0.0000578908],"genre_scores_gemma":[0.00003690955,0.0009115734,0.02061603,0.002073404,0.0005369352,0.0002040639,0.9752058,0.00003089244,0.0003843256],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04455781,"threshold_uncertainty_score":0.9999022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04008777971554453,"score_gpt":0.3454199076068783,"score_spread":0.3053321278913338,"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."}}