{"id":"W1994817274","doi":"10.1007/s11276-015-0910-7","title":"A MAC transmission strategy in sparse Vehicular Delay-Tolerant Sensor Networks","year":2015,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wireless sensor network; Computer network; Energy consumption; Transmission (telecommunications); Efficient energy use; Throughput; Data transmission; Transmission delay; Latency (audio); Real-time computing; Wireless; Network packet; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001171341,0.0007306431,0.0008540936,0.0003214224,0.0002054891,0.0003892492,0.001788287,0.0007376746,0.00001787022],"category_scores_gemma":[0.00001417452,0.0006980732,0.0002472907,0.001989067,0.0001673529,0.0006038023,0.0003483013,0.001307061,0.0000389993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650063,"about_ca_system_score_gemma":0.0002024625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002407054,"about_ca_topic_score_gemma":0.0001618159,"domain_scores_codex":[0.9942326,0.0006378915,0.001044887,0.001402576,0.0009109678,0.001771104],"domain_scores_gemma":[0.9969139,0.0002474558,0.0002994015,0.001422636,0.0002374738,0.0008790975],"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":[0.00008837041,0.000223274,0.0007731345,0.00001025029,0.00002852564,0.000897612,0.0002684618,0.9315145,0.00003105295,0.004171623,0.001270978,0.06072222],"study_design_scores_gemma":[0.001979603,0.0001624928,0.0005486361,0.0002634668,0.00001854709,0.0001432777,0.00007736715,0.9923058,0.00006534356,0.0001342749,0.003463979,0.0008372665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1314635,0.003137074,0.8611684,0.0003811732,0.001077775,0.0004703899,0.000001222158,0.0005599035,0.001740602],"genre_scores_gemma":[0.9895929,0.0004292381,0.008278063,0.0005370331,0.0006455702,0.00006257137,0.00003619066,0.0001002157,0.0003182965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8581293,"threshold_uncertainty_score":0.9995471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02156714814641306,"score_gpt":0.2328372331185261,"score_spread":0.2112700849721131,"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."}}