{"id":"W2918055950","doi":"10.5281/zenodo.50587","title":"pyGeno: a Python Package for Precision Medicine (1.2.8)","year":2016,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"","keywords":"Python (programming language); Computer science; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005699234,0.0001085031,0.0001139379,0.0001472504,0.001233914,0.0003165973,0.001353033,0.00003411856,0.001024377],"category_scores_gemma":[0.0003881493,0.0000824095,0.00004891149,0.0004977509,0.00009889638,0.0003058446,0.0007783046,0.00006877891,0.002558449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008059099,"about_ca_system_score_gemma":0.000004964352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003566924,"about_ca_topic_score_gemma":1.001939e-7,"domain_scores_codex":[0.9986587,0.0001136019,0.0002201271,0.0004370244,0.0003221717,0.0002483613],"domain_scores_gemma":[0.9983674,0.0001562361,0.000100558,0.0006119294,0.0006186247,0.000145227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001502798,0.00005654391,3.71821e-7,0.00001146837,0.00001168982,8.863569e-7,0.0003936074,0.000007558996,0.02535691,0.3223365,0.1286099,0.5231996],"study_design_scores_gemma":[0.0005741795,0.0002137018,0.0002530652,0.00003464031,0.000004555388,0.00002108589,0.00001552419,0.001506519,0.0009998899,0.05926971,0.9369789,0.00012821],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008356039,0.00003308427,0.9639928,0.008321808,0.0001030604,0.0004582228,0.00009009946,0.0006383976,0.02552691],"genre_scores_gemma":[0.9803185,0.00006711233,0.01527915,0.0004791268,0.000514693,5.353324e-7,0.0005467383,0.0008766837,0.001917456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9794829,"threshold_uncertainty_score":0.9998888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04121460125723893,"score_gpt":0.273688615367801,"score_spread":0.2324740141105621,"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."}}