{"id":"W2617272232","doi":"10.3968/9557","title":"Study on News Reporting Patterns Based on AR Technology","year":2017,"lang":"en","type":"article","venue":"Canadian social science","topic":"Innovative Educational Techniques","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Technological convergence; Presentation (obstetrics); Neutrality; Computer science; News media; Profit (economics); Data science; Advertising; Business; Political science; Telecommunications; Law; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002174363,0.0002551407,0.0003168127,0.009782915,0.0009452119,0.003233231,0.0005650618,0.0005371348,0.003484994],"category_scores_gemma":[0.02317872,0.0001817498,0.0004285541,0.01148039,0.0005273967,0.002698528,0.0008560605,0.0006539838,0.001645843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006590544,"about_ca_system_score_gemma":0.0005693319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004314786,"about_ca_topic_score_gemma":0.003695449,"domain_scores_codex":[0.9952946,0.001178476,0.0009122119,0.0006660888,0.001550597,0.0003979536],"domain_scores_gemma":[0.9615945,0.01501476,0.01292459,0.00154153,0.007851987,0.001072538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000277484,0.00009097149,0.8753752,0.000369999,0.00008874964,0.00108685,0.02516838,0.0001847395,0.002663,0.002800233,0.005334333,0.08656003],"study_design_scores_gemma":[0.000006321045,0.0001272738,0.929838,0.0001451013,0.00009492629,0.001915015,0.03650026,0.001647453,0.001706611,0.0005423259,0.0274204,0.00005632629],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674016,0.001315018,0.002641089,0.0007757811,0.0001056285,0.0001282193,0.002898263,0.000143495,0.02459098],"genre_scores_gemma":[0.9887851,0.001144462,0.001729621,0.0001032467,0.0001668921,0.00007643135,0.002926094,0.00006287966,0.005005185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009782915,"threshold_uncertainty_score":0.01165849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1103578287534136,"score_gpt":0.4464881154295539,"score_spread":0.3361302866761403,"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."}}