{"id":"W2964284010","doi":"10.1109/iwcmc.2019.8766720","title":"Cluster Aware Mobility Encounter Dataset Enlargement","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Gnowit (Canada)","funders":"","keywords":"Computer science; Cluster (spacecraft); Data mining; Synthetic data; Statistical model; Machine learning; Artificial intelligence","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000792723,0.0004203458,0.0004845973,0.0000858122,0.00007669793,0.0004592541,0.00245152,0.0003773833,0.0008728501],"category_scores_gemma":[0.000003490084,0.0003460578,0.0001631541,0.00008986257,0.00005399838,0.0002852511,0.007162075,0.0007091701,0.000960185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001060258,"about_ca_system_score_gemma":0.0003303137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001203417,"about_ca_topic_score_gemma":0.00002298216,"domain_scores_codex":[0.9968901,0.000118725,0.0005688892,0.001335665,0.0005828062,0.0005038708],"domain_scores_gemma":[0.9957209,0.0001312985,0.0002169737,0.003609916,0.0001244859,0.0001964204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001458099,0.00023068,0.0006690804,0.0002059393,0.00008788367,0.00004767156,0.0001523809,0.001626129,5.583647e-7,0.00204012,0.9737643,0.02116068],"study_design_scores_gemma":[0.0002656245,0.00003596567,0.0001116384,0.00006959017,0.00002175262,0.000008458822,0.00001534727,0.8632154,0.000005161933,0.002559811,0.1332319,0.0004593291],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002415584,0.00009864092,0.9798262,0.000923235,0.003625294,0.0007478814,0.00132651,0.0001989389,0.01301181],"genre_scores_gemma":[0.9389614,0.0001281366,0.03228733,0.01226339,0.0006369019,0.0001484502,0.00824101,0.00004213342,0.007291215],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9475388,"threshold_uncertainty_score":0.9998991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02944996651813995,"score_gpt":0.2767360278044718,"score_spread":0.2472860612863319,"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."}}