{"id":"W4393469418","doi":"10.5281/zenodo.3555321","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":"Zenodo (CERN European Organization for Nuclear Research)","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; Molecular biology; Chromatography; Biology; Biochemistry; Physics; Recombinant DNA; 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":[0.002258063,0.003991141,0.002117697,0.002599323,0.001512933,0.002187989,0.004590542,0.003361505,0.02107626],"category_scores_gemma":[0.003765389,0.0009530336,0.002426517,0.003271934,0.0009044334,0.0009406265,0.001850052,0.003174602,0.0480962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002237281,"about_ca_system_score_gemma":0.002983471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02487559,"about_ca_topic_score_gemma":0.04879618,"domain_scores_codex":[0.998247,0.0003162918,0.0001703414,0.0005576632,0.0004097115,0.0002990595],"domain_scores_gemma":[0.9984391,0.0003780368,0.0001256723,0.0005245589,0.000339605,0.0001929599],"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":[0.0003519291,0.0002196064,0.002068559,0.001249629,0.0001342867,0.0001005212,0.0000381204,0.001541812,0.002233139,0.0005598449,0.9845265,0.00697608],"study_design_scores_gemma":[0.0013832,0.0002605164,0.01500986,0.0004481855,0.0002268579,0.0005125248,0.0001933635,0.004926274,0.01066494,0.003078415,0.9631583,0.0001375598],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001883448,0.0002738681,0.0005829554,0.0002354628,0.0001072071,0.00006944944,0.9945804,0.001343045,0.0009240906],"genre_scores_gemma":[0.0009001488,0.00006845523,0.0007917772,0.0000639743,0.000005755815,0.00008901616,0.9974171,0.00006604278,0.0005976724],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02487559,"threshold_uncertainty_score":0.07050711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140232221909292,"score_gpt":0.2498159236034824,"score_spread":0.2357927014125532,"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."}}