{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001257609,0.0006610583,0.0005489112,0.00169418,0.0007804526,0.000894325,0.001959554,0.001187166,0.001976536],"category_scores_gemma":[0.005768182,0.0001924666,0.000716174,0.002685708,0.0003429541,0.001208047,0.001428212,0.001052921,0.001143299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008878515,"about_ca_system_score_gemma":0.001210489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006204817,"about_ca_topic_score_gemma":0.008768919,"domain_scores_codex":[0.9985044,0.0002752093,0.0001931846,0.0003684188,0.0004766374,0.0001822558],"domain_scores_gemma":[0.9974611,0.0004686156,0.0002077884,0.0008597003,0.0007605845,0.0002423092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001923428,0.001606546,0.0861979,0.002085754,0.0005991803,0.001781845,0.0009922142,0.1700633,0.0171122,0.02270881,0.5087082,0.1862206],"study_design_scores_gemma":[0.0004185692,0.0007826483,0.08127356,0.0002401474,0.0001771529,0.002224369,0.001642844,0.4692269,0.02674829,0.01470114,0.4023127,0.0002516389],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3537333,0.0007562098,0.08891489,0.00247627,0.001123736,0.002569461,0.5225877,0.01249625,0.01534221],"genre_scores_gemma":[0.3419122,0.0002958257,0.05436185,0.0002816737,0.0001091058,0.001257686,0.5983797,0.0002900229,0.003111892],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.006204817,"threshold_uncertainty_score":0.01233739,"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."}}