{"id":"W1500921800","doi":"10.5539/cis.v8n3p13","title":"Collision Detection in Wireless Sensor Networks Through Pseudo-Coded ON-OFF Pilot Periods per Packet: A Novel Low-Complexity and Low-Power Design Technique","year":2015,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Taif University","keywords":"Computer science; Network packet; Wireless sensor network; Decoding methods; Computer network; Collision; Demodulation; Collision problem; Wireless; Key distribution in wireless sensor networks; Real-time computing; Wireless network; Telecommunications; Computer security; Channel (broadcasting)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001644681,0.0002964441,0.0002883431,0.0004440702,0.0004507602,0.0009631447,0.000739233,0.0001294343,0.000001624406],"category_scores_gemma":[0.0000527038,0.0002625093,0.00003148353,0.001580804,0.0006178593,0.006442229,0.0005342244,0.0003229466,0.00001156427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001860028,"about_ca_system_score_gemma":0.0001601292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003617215,"about_ca_topic_score_gemma":0.000006530807,"domain_scores_codex":[0.9974931,0.0001171564,0.0005539214,0.0005632931,0.0007248828,0.0005476294],"domain_scores_gemma":[0.9983906,0.0001583213,0.000229108,0.0005507271,0.0004125623,0.0002587323],"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":[0.0001821476,0.0002600284,0.0002095843,0.0000394837,0.000005827846,0.000006348335,0.007144823,0.9229158,0.003926705,0.02254155,0.0002038172,0.04256395],"study_design_scores_gemma":[0.0009070778,0.0005002856,0.003226368,0.0001182736,0.000001593991,0.0000850474,0.00007262982,0.9894142,0.004986351,0.000126858,0.0002233324,0.0003380427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2896136,0.00001581565,0.7089048,0.0001088389,0.0004705708,0.0004222031,0.000001325677,0.0001361275,0.0003266716],"genre_scores_gemma":[0.8638416,0.00004436261,0.135142,0.000868629,0.00005407546,0.00003404513,0.000002529719,0.000008245595,0.000004503032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5742279,"threshold_uncertainty_score":0.9999827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03467235676968721,"score_gpt":0.2550599603086868,"score_spread":0.2203876035389996,"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."}}