{"id":"W3032715233","doi":"10.1109/nca51143.2020.9306744","title":"Introducing Network Coding to RPL: The Chained Secure Mode (CSM)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Replay attack; Computer science; Computer network; Routing protocol; Resilience (materials science); Lossy compression; Computer security; Coding (social sciences); Routing (electronic design automation); Authentication (law)","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.0006183806,0.0004580599,0.0005240248,0.00006978905,0.0003434589,0.0007664533,0.00326037,0.0002612857,0.00004367191],"category_scores_gemma":[0.0001696798,0.000322131,0.0002209892,0.0007894219,0.00003052481,0.0001141631,0.006517226,0.001186982,0.0001156398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008837984,"about_ca_system_score_gemma":0.0001664301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002085694,"about_ca_topic_score_gemma":0.00008958171,"domain_scores_codex":[0.9967976,0.0001481728,0.0004605666,0.001387911,0.0004746334,0.00073107],"domain_scores_gemma":[0.9970722,0.0003699763,0.0001862489,0.001980568,0.0001164672,0.0002745831],"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":[0.00001221017,0.00001284281,0.0001539399,0.0000423102,0.00007279924,0.00001744235,0.001987655,0.681175,0.00001506857,0.1114414,0.1906082,0.01446111],"study_design_scores_gemma":[0.0001437232,0.00005018424,0.0002230163,0.0002019716,0.00002288055,0.000008157969,0.00002594682,0.9433444,0.00004556064,0.03470691,0.02066124,0.0005660129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004403415,0.0003963386,0.9437851,0.0482105,0.003483862,0.000638259,0.000004945452,0.001018373,0.002022337],"genre_scores_gemma":[0.6235265,0.0001724019,0.3383117,0.0260519,0.01072076,0.0002159531,0.00003519056,0.00009785098,0.0008676815],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6230862,"threshold_uncertainty_score":0.9999231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02488323660102211,"score_gpt":0.2600343332439921,"score_spread":0.23515109664297,"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."}}