{"id":"W2982118686","doi":"10.34726/lbs2019.57","title":"Consistency Across Geosocial Media Platforms","year":2019,"lang":"en","type":"article","venue":"VBN Forskningsportal (Aalborg Universitet)","topic":"Public Relations and Crisis Communication","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Consistency (knowledge bases); Computer science; Information retrieval; Geography; 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":[0.02083063,0.000327196,0.0005373058,0.006306082,0.001547409,0.005561445,0.001324188,0.0008495224,0.004540448],"category_scores_gemma":[0.1120398,0.000539182,0.0007690038,0.008553558,0.001959496,0.006259402,0.004971353,0.001083896,0.001047041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216672,"about_ca_system_score_gemma":0.0010082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009611149,"about_ca_topic_score_gemma":0.01108962,"domain_scores_codex":[0.9712561,0.01401178,0.003092569,0.004548423,0.005873458,0.001217694],"domain_scores_gemma":[0.8655553,0.0767574,0.01674821,0.01980078,0.02008533,0.001052969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000492564,0.0001321469,0.8607535,0.001022292,0.0008010653,0.0002458383,0.0278316,0.002063469,0.002576268,0.01483584,0.007727478,0.08151791],"study_design_scores_gemma":[0.00003636756,0.0001781798,0.8842239,0.0009215844,0.0002474518,0.000386538,0.03682398,0.006045221,0.003900866,0.01306772,0.05399904,0.000169193],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9252208,0.001211344,0.01669901,0.00240945,0.0002673893,0.0003618004,0.014436,0.0002164723,0.0391777],"genre_scores_gemma":[0.9849175,0.0002253002,0.005919185,0.0002123675,0.00006143637,0.0002720468,0.006941963,0.00009979498,0.001350426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02083063,"threshold_uncertainty_score":0.1101642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346407051926249,"score_gpt":0.2789090871703114,"score_spread":0.2654450166510489,"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."}}