{"id":"W4393660813","doi":"10.5281/zenodo.3383264","title":"Large-scale purification of Q23 and Q54 HTT-HAP40 from Sf9 expression system 2019/07/29","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sf9; Scale (ratio); Expression (computer science); Biology; Physics; Computer science; Genetics; Quantum mechanics","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":[0.001835574,0.002672853,0.002425979,0.003192639,0.001292986,0.002722445,0.003431619,0.003591227,0.1071491],"category_scores_gemma":[0.008077779,0.0009698143,0.002084833,0.005101536,0.00050928,0.00141365,0.001783815,0.002213556,0.1214926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001693008,"about_ca_system_score_gemma":0.003384673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01423402,"about_ca_topic_score_gemma":0.02440206,"domain_scores_codex":[0.9987308,0.000221854,0.0001760483,0.0003995073,0.0002888014,0.0001828801],"domain_scores_gemma":[0.9969839,0.001331985,0.000277002,0.0006023802,0.0005358595,0.0002687757],"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":[0.0002429826,0.00003678238,0.0008717423,0.002708456,0.00009947936,0.00003741706,0.0000217929,0.0003643521,0.0006884415,0.0004650913,0.9913408,0.003122727],"study_design_scores_gemma":[0.001558631,0.00008253353,0.007713798,0.0009144682,0.0002018347,0.0001349037,0.00006844783,0.0009152648,0.002191508,0.002832177,0.9832942,0.00009217235],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001314695,0.0001430275,0.00009880643,0.00008714484,0.0000237383,0.00001489228,0.9985129,0.00059216,0.0003957373],"genre_scores_gemma":[0.0004064296,0.0001046282,0.0003619416,0.0000781041,0.000004755429,0.00008224991,0.9985459,0.0001052331,0.0003107695],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1071491,"threshold_uncertainty_score":0.3584495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03773238690198399,"score_gpt":0.3132280944794117,"score_spread":0.2754957075774277,"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."}}