{"id":"W3012406517","doi":"10.5220/0009179502260232","title":"Sharing Bioinformatic Data for Machine Learning: Maximizing Interoperability through License Selection","year":2020,"lang":"en","type":"article","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Interoperability; License; Selection (genetic algorithm); Machine learning; Data sharing; Artificial intelligence; World Wide Web; Operating system","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":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004824851,0.0001205945,0.0002063363,0.00007975953,0.0003522093,0.001090499,0.002163566,0.00002653545,0.00059533],"category_scores_gemma":[0.008967466,0.00008354718,0.00006202062,0.0008564885,0.00003954069,0.001346643,0.002596488,0.0001475952,0.0004461253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000252323,"about_ca_system_score_gemma":0.00002096108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001151796,"about_ca_topic_score_gemma":0.00005505801,"domain_scores_codex":[0.997395,0.00007235055,0.0006727847,0.0009782671,0.0006320344,0.0002495001],"domain_scores_gemma":[0.9977435,0.0005414587,0.0001751442,0.0012818,0.0001576981,0.0001004397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003650486,0.0002154071,0.02875799,0.0002246996,0.0001251015,0.000003195416,0.01242343,0.01529413,0.0005972418,0.005680134,0.4489168,0.4873968],"study_design_scores_gemma":[0.0002049879,0.0000602625,0.0002009266,0.000006232716,0.00000802677,0.000001550979,0.001110294,0.773108,0.00008488215,0.0007025746,0.2244226,0.00008962651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03985521,0.00002930202,0.9462751,0.005356413,0.0005393047,0.0004204549,0.00005452026,0.0003127574,0.007156929],"genre_scores_gemma":[0.9304458,0.000003929631,0.06468726,0.001813729,0.0001361304,0.000007099672,0.0002192061,0.00001173892,0.002675085],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8905906,"threshold_uncertainty_score":0.9999465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4897343148360663,"score_gpt":0.4303870654464238,"score_spread":0.05934724938964253,"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."}}