{"id":"W2810930981","doi":"10.1109/uic-atc.2017.8397543","title":"Sensing language relationships from social media","year":2017,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Social media; Locality; Key (lock); Social network (sociolinguistics); Preference; Cluster analysis; Scale (ratio); Natural language processing; World Wide Web; Artificial intelligence; Data science; Linguistics; Geography","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":[0.0005168883,0.0004492782,0.0003184,0.00417829,0.0005218855,0.001462989,0.0004038885,0.0006732591,0.001282993],"category_scores_gemma":[0.004621356,0.0002192762,0.0002614803,0.003136134,0.0003385109,0.002452126,0.001282926,0.0004755353,0.0008147648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004189177,"about_ca_system_score_gemma":0.0002275767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004678439,"about_ca_topic_score_gemma":0.009394308,"domain_scores_codex":[0.9992194,0.0002725797,0.00005320039,0.0001768661,0.0001906731,0.00008721289],"domain_scores_gemma":[0.9977202,0.001171414,0.000483177,0.000151591,0.0003422869,0.0001313373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008854955,0.0006106886,0.6819971,0.0008712797,0.0004805338,0.000993571,0.006983765,0.0159865,0.03587988,0.007571553,0.01429083,0.2334488],"study_design_scores_gemma":[0.00005070215,0.0002996242,0.7496095,0.0001881259,0.0002064809,0.0009686324,0.01595985,0.1750765,0.01406872,0.01198299,0.03144431,0.0001445748],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742059,0.000527796,0.008992997,0.0006388811,0.00005850306,0.00008899027,0.007105862,0.0002017344,0.008179378],"genre_scores_gemma":[0.9886463,0.0003369403,0.006728913,0.00008075145,0.00009452692,0.00007014589,0.003144237,0.00002065989,0.0008775872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004678439,"threshold_uncertainty_score":0.009302378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08382568889305088,"score_gpt":0.3502049779917798,"score_spread":0.2663792890987289,"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."}}