{"id":"W4393611621","doi":"10.5281/zenodo.3555322","title":"Large-scale purification of Q23 HTT-HAP40 from Sf9 expression system with contaminating nucleic acid material 2019/09/16","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Nucleic acid; Sf9; Scale (ratio); Chemistry; Expression (computer science); Molecular biology; Chromatography; Computational biology; Biology; Biochemistry; Computer science; Geography; Gene; Recombinant DNA; Cartography","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":[0.002383481,0.004007413,0.00241648,0.002536836,0.001801161,0.002569798,0.005132443,0.003929173,0.03501397],"category_scores_gemma":[0.005039737,0.001097435,0.002341048,0.002985158,0.0008338609,0.001075944,0.001912051,0.003425661,0.06244981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002477508,"about_ca_system_score_gemma":0.003406218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02125718,"about_ca_topic_score_gemma":0.04341371,"domain_scores_codex":[0.9983823,0.0002976738,0.0001440044,0.0005321346,0.0003643689,0.000279464],"domain_scores_gemma":[0.9982771,0.0005362901,0.000138675,0.000514756,0.0003265414,0.0002067095],"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":[0.0003341458,0.0001369628,0.001465271,0.001592961,0.0001275871,0.00007115996,0.00003594071,0.0009858966,0.002197048,0.0005979245,0.9872407,0.005214467],"study_design_scores_gemma":[0.001763077,0.0002067377,0.01094977,0.0005351936,0.0002555135,0.0003872985,0.0001287484,0.003794819,0.01049696,0.003575293,0.9677728,0.0001337914],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00111622,0.0002570238,0.0005178328,0.0002389907,0.00008804033,0.00005764214,0.9950595,0.001837739,0.0008270991],"genre_scores_gemma":[0.0008415774,0.00008977758,0.001029313,0.0001078738,0.000006759369,0.0001232809,0.9970624,0.0001388186,0.0006002572],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03501397,"threshold_uncertainty_score":0.1171334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282459550546452,"score_gpt":0.262656469747813,"score_spread":0.2498318742423485,"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."}}