{"id":"W4394553973","doi":"10.6084/m9.figshare.19498035","title":"Additional file 1 of LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Guelph","funders":"","keywords":"Landmark; Computer science; Selection (genetic algorithm); Throughput; Computational biology; Data mining; Machine learning; Artificial intelligence; Pattern recognition (psychology); Biology","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":[0.002569059,0.002045672,0.001614379,0.00237904,0.0008102198,0.002192938,0.002613981,0.00187497,0.4712683],"category_scores_gemma":[0.01628446,0.0007012482,0.001549176,0.00386497,0.000514015,0.001313004,0.001285441,0.001811835,0.1220621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001179688,"about_ca_system_score_gemma":0.002105592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006127057,"about_ca_topic_score_gemma":0.01497353,"domain_scores_codex":[0.9989358,0.0002416838,0.0001210053,0.0004130715,0.0001707691,0.0001176966],"domain_scores_gemma":[0.9900647,0.007473392,0.0003630008,0.0008983799,0.0008834469,0.0003171492],"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":[0.0002682473,0.00007255663,0.001898393,0.00221203,0.000119856,0.00004844338,0.00003006576,0.001119908,0.0002444829,0.0005879175,0.9877608,0.005637405],"study_design_scores_gemma":[0.004111378,0.0002737009,0.01502933,0.001183612,0.0003724797,0.0004190091,0.0001380108,0.004862733,0.001736274,0.01229792,0.9594258,0.0001496451],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000101514,0.00003355101,0.0002019187,0.000036572,0.00001397746,0.00002370322,0.9990802,0.0003134663,0.0001950982],"genre_scores_gemma":[0.00189728,0.00007102168,0.001888048,0.0001275335,0.00002446409,0.0004466885,0.9940509,0.0003432917,0.001150682],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4712683,"threshold_uncertainty_score":0.7541716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0447128627911663,"score_gpt":0.2593171461284042,"score_spread":0.2146042833372379,"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."}}