{"id":"W2294671746","doi":"10.1007/978-3-319-11569-6_66","title":"Mobility Models-Based Performance Evaluation of the History Based Prediction for Routing Protocol for Infrastructure-Less Opportunistic Networks","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer network; Waypoint; Communication source; Routing protocol; Mobility model; Latency (audio); Network packet; Exploit; Overhead (engineering); Topology (electrical circuits); Distributed computing; Real-time computing; Engineering; Telecommunications; Computer security","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":[],"consensus_categories":[],"category_scores_codex":[0.001709185,0.0002807009,0.0003897095,0.0001154184,0.0008131572,0.00008418118,0.001734834,0.0002609974,6.269977e-7],"category_scores_gemma":[0.00005580507,0.000204631,0.0002723265,0.0001307542,0.0004395805,0.0002465451,0.0003060175,0.0003027734,1.572842e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002380215,"about_ca_system_score_gemma":0.001007234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007398168,"about_ca_topic_score_gemma":0.00001430127,"domain_scores_codex":[0.9982229,0.00003064919,0.0008688849,0.0002059233,0.0004320337,0.0002396475],"domain_scores_gemma":[0.9967974,0.0005849921,0.001064845,0.0008175158,0.0006879047,0.00004733253],"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":[0.00000780802,0.00001145927,0.000003656035,0.0003205153,0.00002313915,2.467337e-9,0.0001697495,0.9288169,6.760254e-7,0.01610683,0.0001102916,0.05442897],"study_design_scores_gemma":[0.0006728997,0.0001084391,0.00003005636,0.0003945102,0.00009423726,0.00000109884,0.000001270364,0.9910872,0.000009603558,0.001330394,0.006071082,0.0001992025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002785364,0.00002791856,0.9763141,0.0001434231,0.0008016393,0.02215772,0.0000804732,0.0000417009,0.0004052302],"genre_scores_gemma":[0.1292197,0.000003677183,0.8592843,0.0002432487,0.0002257584,0.01087615,0.00009790059,0.00002533393,0.00002390758],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1291919,"threshold_uncertainty_score":0.8344608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07137973589297492,"score_gpt":0.2643832852934064,"score_spread":0.1930035494004315,"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."}}