{"id":"W6912060283","doi":"10.5281/zenodo.15756545","title":"Appendix for RevealNet","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Appendix; Calculus (dental); Term (time)","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":[0.002983409,0.001151621,0.0009720094,0.00506197,0.001089388,0.002925497,0.001700333,0.001249777,0.7844251],"category_scores_gemma":[0.04056751,0.001107758,0.0006331218,0.005517319,0.0005030466,0.005025473,0.002819991,0.001644659,0.540845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736541,"about_ca_system_score_gemma":0.002934377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005730323,"about_ca_topic_score_gemma":0.007212622,"domain_scores_codex":[0.9976906,0.0004694787,0.0003617562,0.0004289837,0.0008400197,0.0002091381],"domain_scores_gemma":[0.9661503,0.01513909,0.001665575,0.004394782,0.0116773,0.0009729341],"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":[0.00003346964,0.00001481119,0.0001952793,0.0001534598,0.000004989671,0.00001782056,0.00001751779,0.000118512,0.00007555207,0.001246069,0.9932413,0.0048812],"study_design_scores_gemma":[0.00006670002,0.00001382107,0.0008194906,0.0001749209,0.000009080746,0.00009311816,0.00006167438,0.0003558724,0.0002803723,0.004974507,0.9931304,0.00002009935],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0004448192,0.0001297476,0.006432957,0.001659876,0.0009060442,0.0003065978,0.9395103,0.008302737,0.04230683],"genre_scores_gemma":[0.004328488,0.0003269805,0.00946643,0.002344354,0.0006658331,0.001066433,0.9166369,0.0109401,0.05422445],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7844251,"threshold_uncertainty_score":0.3074914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02249427381196908,"score_gpt":0.2464608606714601,"score_spread":0.2239665868594911,"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."}}