{"id":"W4393771338","doi":"10.5281/zenodo.3891804","title":"CCLid: A toolkit to authenticate the genotype and stability of cancer cell lines","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cancer Research and Treatments","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Genotype; Stability (learning theory); Computer science; Biology; Genetics; 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":[0.002687024,0.001164712,0.00159016,0.003499763,0.0008284872,0.002252574,0.003314643,0.001493161,0.02035328],"category_scores_gemma":[0.006661269,0.0007815623,0.001218219,0.003315048,0.0003773757,0.001089631,0.003293177,0.001632309,0.03334176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001263445,"about_ca_system_score_gemma":0.001617468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007121671,"about_ca_topic_score_gemma":0.01555068,"domain_scores_codex":[0.9984357,0.0002458733,0.0002266564,0.000493477,0.0004392624,0.0001590485],"domain_scores_gemma":[0.9968765,0.000856531,0.0003003226,0.001173087,0.0005629229,0.0002306082],"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":[0.0005673979,0.00008983337,0.008551494,0.002038959,0.0001801513,0.0002061525,0.0001766354,0.003091764,0.007577176,0.003500629,0.9460304,0.02798933],"study_design_scores_gemma":[0.0004231872,0.00007591055,0.01437316,0.0003401352,0.0001125126,0.0004968833,0.0001246089,0.005774197,0.008773084,0.005300344,0.9640887,0.0001173329],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002241438,0.0003440848,0.005190484,0.0001091041,0.00006245773,0.00008468708,0.9819173,0.008476869,0.001573558],"genre_scores_gemma":[0.001618082,0.00009398728,0.004693078,0.00006855407,0.000005921124,0.000272639,0.9922134,0.0004090271,0.0006253488],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02035328,"threshold_uncertainty_score":0.06808853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03043572616883979,"score_gpt":0.2797846463760968,"score_spread":0.249348920207257,"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."}}