{"id":"W7098116954","doi":"","title":"NEPTSim: Simulating NEPTUNE Canada using OMNeT++","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004722308,0.001345966,0.000663341,0.0005015351,0.001090848,0.001250971,0.003625167,0.001099341,0.02904235],"category_scores_gemma":[0.001404995,0.0007848308,0.0009979707,0.0009126863,0.0006817894,0.000941472,0.001117557,0.001223177,0.002707949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002657728,"about_ca_system_score_gemma":0.004588757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4184827,"about_ca_topic_score_gemma":0.4975834,"domain_scores_codex":[0.9997143,0.00004920613,0.0000120133,0.00004264678,0.0001057208,0.00007608849],"domain_scores_gemma":[0.9994453,0.0001921752,0.00002017013,0.00004969838,0.0001830397,0.0001097106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007823076,0.0001944984,0.005080201,0.0004985872,0.000202295,0.0004974222,0.0004417766,0.8601467,0.004774469,0.01101719,0.0892825,0.02708216],"study_design_scores_gemma":[0.0003179705,0.00006185262,0.0006772898,0.00004613774,0.00005216089,0.00005760599,0.0001540138,0.940625,0.003264586,0.002651063,0.05202969,0.00006263318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3422694,0.001072889,0.2056651,0.002368459,0.002078133,0.0008901564,0.07025752,0.1221138,0.2532843],"genre_scores_gemma":[0.6874055,0.0008593613,0.2200441,0.0008489198,0.00007392598,0.0005095681,0.03533119,0.01403899,0.04088852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4184827,"threshold_uncertainty_score":0.8320937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006903279087935032,"score_gpt":0.2989952841613837,"score_spread":0.2920920050734487,"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."}}