{"id":"W4309967608","doi":"10.5281/zenodo.7199741","title":"Data and Codes for Pan-cancer classification of single cells in the tumour microenvironment","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Ontario Institute for Cancer Research","funders":"","keywords":"Cancer; Tumor microenvironment; Cancer research; Computational biology; Biology; Genetics","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":[0.002198903,0.001837811,0.001802088,0.005495356,0.001385787,0.002653359,0.002587221,0.00206976,0.313933],"category_scores_gemma":[0.0160428,0.00122765,0.001828755,0.007217813,0.0008033143,0.001972118,0.002200392,0.003043087,0.1875477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001999828,"about_ca_system_score_gemma":0.003331051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01035515,"about_ca_topic_score_gemma":0.01385516,"domain_scores_codex":[0.99699,0.0002726706,0.0005451616,0.0009047674,0.0009498979,0.0003375035],"domain_scores_gemma":[0.9870745,0.005786588,0.001017455,0.002546276,0.002949557,0.0006255996],"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":[0.0005318169,0.00008178435,0.004177175,0.002130678,0.00007028393,0.0001253905,0.0001539103,0.0008558609,0.004509326,0.001514951,0.9696382,0.01621054],"study_design_scores_gemma":[0.0003241584,0.00009496916,0.0132551,0.0006031135,0.00007140415,0.0004092463,0.0002088502,0.001243515,0.006164713,0.004669269,0.9728568,0.00009895254],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008033551,0.0001002195,0.00239004,0.0001175781,0.0001407814,0.0001218072,0.9883582,0.006125844,0.001842223],"genre_scores_gemma":[0.002422195,0.000146426,0.007092852,0.0002954184,0.00003315796,0.0009220062,0.9809111,0.003827977,0.004348787],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.313933,"threshold_uncertainty_score":0.9785914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04644131848206567,"score_gpt":0.2577346387134055,"score_spread":0.2112933202313398,"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."}}