{"id":"W4292826035","doi":"10.1109/jiot.2022.3201177","title":"RPL Point-to-Point Communication Paths: Analysis and Enhancement","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Overhead (engineering); Computer network; Routing protocol; Multiprotocol Label Switching; Shortest path problem; Routing (electronic design automation); Point-to-point; Path (computing); Network topology; Topology (electrical circuits); Lossy compression; Distributed computing; Quality of service; Mathematics; Graph; Theoretical computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.001423655,0.0001365487,0.0002866535,0.0005029563,0.0002154703,0.0002104617,0.001632263,0.00003061758,0.0001216095],"category_scores_gemma":[0.00003190348,0.00013331,0.0001611887,0.0007200273,0.00004654538,0.0004176711,0.0008844848,0.0005252678,0.000004014454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001645101,"about_ca_system_score_gemma":0.00002865378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001663994,"about_ca_topic_score_gemma":0.00001275823,"domain_scores_codex":[0.9980416,0.0002891015,0.0005628504,0.0002703947,0.0005864998,0.0002496097],"domain_scores_gemma":[0.9985206,0.0001242871,0.0005120374,0.0005707019,0.0001270548,0.0001453844],"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":[0.0002451772,0.001333658,0.005298235,0.0000476289,0.002858868,0.0002016288,0.08528147,0.6904795,0.04945632,0.01869361,0.02504932,0.1210546],"study_design_scores_gemma":[0.0006940066,0.0008024495,0.0008962592,0.0001476798,0.000192284,0.0004392133,0.0006677547,0.9429811,0.04670592,0.00149822,0.004503254,0.0004718414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3435793,0.0002841236,0.6538911,0.001269619,0.000437373,0.0000565048,6.782917e-7,0.00002701515,0.0004542466],"genre_scores_gemma":[0.9468056,0.00007777786,0.05175014,0.0009398825,0.00003164506,0.000008384219,0.00000168101,0.000009231481,0.0003756497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6032263,"threshold_uncertainty_score":0.5436223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009260761465455365,"score_gpt":0.2341378124544521,"score_spread":0.2248770509889967,"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."}}