{"id":"W4394045905","doi":"10.5281/zenodo.1186267","title":"Usp5 Zf-Ubd Fluorescence Polarization Displacement Assay Optimization","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Fluorescence anisotropy; Fluorescence; Displacement (psychology); Polarization (electrochemistry); Computer science; Chemistry; Physics; Optics; Psychology; Physical chemistry","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","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004343141,0.0002555353,0.0001607894,0.0001433089,0.001491413,0.0003784519,0.0009367481,0.0002894515,0.001415427],"category_scores_gemma":[0.0004462263,0.0002756364,0.00006848936,0.0003211475,0.0002158998,0.00001633017,0.00124099,0.0002407592,0.000684973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009528182,"about_ca_system_score_gemma":0.000009665501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001422358,"about_ca_topic_score_gemma":9.769424e-7,"domain_scores_codex":[0.9981415,0.0002317005,0.0003031737,0.0006885231,0.0003029645,0.0003320948],"domain_scores_gemma":[0.9980103,0.000006672309,0.0002467629,0.000954764,0.000645893,0.0001355854],"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":[0.00004049516,0.0001047812,1.975277e-7,0.00004056918,0.00003041234,0.000001475049,0.00000915479,0.0001920961,0.01758479,0.00006917895,0.9787181,0.003208775],"study_design_scores_gemma":[0.0002016916,0.0002981486,0.00000621722,0.00004568501,0.00003284329,0.0000388976,0.00001221496,0.0005335521,0.004326412,0.00002659193,0.9941754,0.0003023652],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007456075,0.00006592191,0.1426382,0.0003004532,0.0001464547,0.0008655544,0.8541389,0.0003289318,0.001441071],"genre_scores_gemma":[0.0006993219,0.000493439,0.006109477,0.0001626127,0.000434476,2.383696e-7,0.9910178,0.000674139,0.0004085599],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1368789,"threshold_uncertainty_score":0.9999696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01786816488217127,"score_gpt":0.2687992179057869,"score_spread":0.2509310530236156,"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."}}