{"id":"W6931407048","doi":"10.5281/zenodo.5719209","title":"SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals","year":2021,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Scripting language; Python (programming language); Documentation; Pipeline (software); Software; Interface (matter); Graphical user interface; Download","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003581056,0.0002580778,0.0002992388,0.000373119,0.0008727632,0.00135707,0.001759682,0.0001785242,0.4941975],"category_scores_gemma":[0.0005600118,0.0002907952,0.0000460805,0.0007377274,0.00006579628,0.000226296,0.002360001,0.0002208902,0.02207222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001131253,"about_ca_system_score_gemma":0.00001661414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002188184,"about_ca_topic_score_gemma":0.00002407203,"domain_scores_codex":[0.9973645,0.0003876757,0.0003900661,0.0009661479,0.0005366917,0.0003549475],"domain_scores_gemma":[0.9975532,0.000042079,0.0002085872,0.001508657,0.0004241963,0.0002632995],"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":[0.00002583835,0.0001169743,5.780602e-7,0.00003269914,0.00002129756,0.00002073089,0.0001836942,0.00007111883,0.2301568,0.00009308032,0.7588754,0.0104018],"study_design_scores_gemma":[0.0003090093,0.0000393058,0.00008060688,0.0001175128,0.00003062316,0.00002259611,0.00008935143,0.0006272483,0.002887794,0.00001187493,0.9954801,0.0003039415],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006914093,0.0005489214,0.04490945,0.00362706,0.001805215,0.002303685,0.039406,0.004204832,0.8962808],"genre_scores_gemma":[0.1076044,0.0003503552,0.002887748,0.002198875,0.003831135,3.049731e-7,0.2543652,0.01841135,0.6103506],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4721252,"threshold_uncertainty_score":0.9999544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08423915206747071,"score_gpt":0.2971922243699935,"score_spread":0.2129530723025228,"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."}}