{"id":"W2610137697","doi":"","title":"Data Acquisition and Preparation for Social Network Analysis Based on Email: Lessons Learned","year":2009,"lang":"en","type":"article","venue":"","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social network analysis; Data collection; Social network (sociolinguistics); Computer science; Situation awareness; Information sharing; Descriptive statistics; Social media; World Wide Web; Knowledge management; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04505735,0.001217644,0.001225137,0.004552283,0.002123738,0.005311863,0.003382095,0.001309141,0.01732085],"category_scores_gemma":[0.178977,0.0008279864,0.0009528606,0.004916365,0.001433225,0.005872778,0.003593662,0.003427906,0.01412915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001548,"about_ca_system_score_gemma":0.006204425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00729575,"about_ca_topic_score_gemma":0.009372199,"domain_scores_codex":[0.9610596,0.02597548,0.003176309,0.002311936,0.006542618,0.000934136],"domain_scores_gemma":[0.7574258,0.1264922,0.006181056,0.03477714,0.07024587,0.004878013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008076814,0.0009788984,0.01865243,0.002185772,0.00008614052,0.0009986944,0.01910793,0.002851954,0.01131785,0.005005359,0.1014285,0.8365788],"study_design_scores_gemma":[0.0008482638,0.002793275,0.1057932,0.007330313,0.0002298903,0.001431884,0.07803736,0.06424017,0.04299454,0.05562383,0.6397463,0.0009309073],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1060145,0.0008160244,0.7633439,0.02583124,0.003140813,0.03131237,0.02701976,0.01595589,0.02656549],"genre_scores_gemma":[0.1078581,0.0006259434,0.8520396,0.001639019,0.0007192412,0.01657806,0.008655005,0.002088674,0.009796454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04505735,"threshold_uncertainty_score":0.2382888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09734179078473493,"score_gpt":0.3608016867227839,"score_spread":0.263459895938049,"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."}}