{"id":"W4386970185","doi":"10.17762/ijritcc.v11i8s.7202","title":"Congestion Detection and Mitigation Technique for Multi-Hop Communication in WSN","year":2023,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer network; Hop (telecommunications); Computer science; Network packet; Node (physics); Network congestion; Wireless sensor network; Transmission (telecommunications); Real-time computing; Telecommunications; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0007016804,0.0004347989,0.0004902084,0.0008141908,0.0005309554,0.0003989266,0.0008512054,0.0004099878,0.0005316044],"category_scores_gemma":[0.001284262,0.000214982,0.0005554111,0.000594901,0.0002998861,0.001058418,0.0004562779,0.00051569,0.0001399088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003531377,"about_ca_system_score_gemma":0.0005253257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001166422,"about_ca_topic_score_gemma":0.001270253,"domain_scores_codex":[0.9995995,0.00009608085,0.00003466515,0.00007163108,0.0001582479,0.00003983141],"domain_scores_gemma":[0.9994878,0.000158652,0.00007612581,0.00005231646,0.0002027918,0.0000223032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003414431,0.0002954019,0.004861106,0.001033084,0.0002225485,0.001188316,0.0007385292,0.1714423,0.2017024,0.02210113,0.006554085,0.5895196],"study_design_scores_gemma":[0.00003688172,0.0006636492,0.003662313,0.00008855231,0.0001196158,0.00120206,0.0002349761,0.9042003,0.06905836,0.006262423,0.0143649,0.0001059139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03225569,0.001676913,0.9631138,0.0002481155,0.0002435745,0.0001549944,0.00003595456,0.0005536644,0.001717285],"genre_scores_gemma":[0.7622432,0.001745955,0.2321612,0.0001262394,0.0001331805,0.000200626,0.0001042602,0.00005427158,0.003230932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001166422,"threshold_uncertainty_score":0.003710866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05082698988786589,"score_gpt":0.3481712521050269,"score_spread":0.297344262217161,"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."}}