{"id":"W6940049826","doi":"10.6084/m9.figshare.26986033.v1","title":"Additional file 1 of Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children’s Health Research Institute; London Health Sciences Centre; Lawson Health Research Institute; Western University","funders":"","keywords":"Table (database); Feature selection; Feature (linguistics); Function (biology); Proteomics; Selection (genetic algorithm); Expression (computer science)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001860275,0.00141818,0.001572676,0.002264329,0.0009016306,0.0018293,0.001960282,0.001232802,0.8506017],"category_scores_gemma":[0.02366952,0.0005806003,0.0009384433,0.003034846,0.000340103,0.00167697,0.0009995283,0.001151335,0.2184844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016576,"about_ca_system_score_gemma":0.001874127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003902799,"about_ca_topic_score_gemma":0.00752888,"domain_scores_codex":[0.999272,0.0001242767,0.0001097515,0.0002290679,0.0001687532,0.00009608163],"domain_scores_gemma":[0.9823989,0.01371481,0.0008147378,0.000961685,0.001680386,0.0004293721],"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":[0.0005815591,0.0001313086,0.002504408,0.003628699,0.00008297422,0.0001068121,0.00004950319,0.0005380374,0.000470969,0.0006102551,0.9798709,0.01142457],"study_design_scores_gemma":[0.005601935,0.0006184172,0.03438979,0.003444114,0.000360284,0.001343404,0.0004105594,0.004713491,0.00339511,0.01500363,0.9304377,0.0002815255],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002188976,0.00003386318,0.0005023416,0.00008959328,0.00002750176,0.00006139203,0.9980776,0.0005105696,0.0004781697],"genre_scores_gemma":[0.006292369,0.0001657553,0.004914526,0.0005895713,0.0001081129,0.0012413,0.9795305,0.001252144,0.005905702],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8506017,"threshold_uncertainty_score":0.2130985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04401553055089534,"score_gpt":0.3202204653830712,"score_spread":0.2762049348321758,"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."}}