{"id":"W4251565148","doi":"10.32920/ryerson.14663643.v1","title":"RAIMA: a Framework for the Design and Analysis of Self-Adaptive Egocentric Social Networks","year":2021,"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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Semantics (computer science); Social network (sociolinguistics); Context (archaeology); Inference; Visualization; Matching (statistics); Data science; Distributed computing; Human–computer interaction; World Wide Web; Artificial intelligence; Social media","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":[0.0009099051,0.0003106364,0.0008392306,0.000182428,0.0002921484,0.0003507273,0.001006118,0.0005352421,0.00002888148],"category_scores_gemma":[0.00001863471,0.000223289,0.0005061581,0.001256357,0.00009796625,0.00007955373,0.001352105,0.0006153821,2.835021e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005005167,"about_ca_system_score_gemma":0.0003216536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000263302,"about_ca_topic_score_gemma":0.000006138693,"domain_scores_codex":[0.9978071,0.000234686,0.0004952443,0.0007443316,0.00032894,0.0003897053],"domain_scores_gemma":[0.9954317,0.002972296,0.0004385135,0.0007218224,0.0003336653,0.0001020613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001741519,0.000414682,0.0005857839,0.0001351361,0.01799354,0.00006476221,0.007284637,0.1973183,4.098188e-7,0.3686358,0.006398667,0.4009942],"study_design_scores_gemma":[0.0001520664,0.00003735826,0.0006020982,0.00003078744,0.002117433,0.000002187976,0.000147591,0.9858246,0.000001737925,0.01077821,0.00005952107,0.0002463461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001094203,0.002368224,0.9953957,0.0005606007,0.000562647,0.0006785757,0.00001059811,0.0001011753,0.0002131271],"genre_scores_gemma":[0.4535257,0.0006165241,0.5450908,0.0003754216,0.0002264224,0.0000784501,0.00002017251,0.00001270387,0.00005376485],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7885064,"threshold_uncertainty_score":0.9105461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04404787783576872,"score_gpt":0.2737052772332902,"score_spread":0.2296573993975215,"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."}}