{"id":"W2101143571","doi":"10.1016/j.ipm.2013.08.005","title":"You have e-mail, what happens next? Tracking the eyes for genre","year":2013,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Key Research and Development Program of China; Engineering and Physical Sciences Research Council; Arts and Humanities Research Council; National Natural Science Foundation of China","keywords":"Disk formatting; Computer science; Metric (unit); Eye tracking; Information retrieval; Natural language processing; Representation (politics); Interpretation (philosophy); Tracking (education); Block (permutation group theory); Artificial intelligence; World Wide Web; Psychology","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":[0.00107685,0.0002551253,0.0002225192,0.00202859,0.0004831741,0.001309084,0.0002142027,0.0005033332,0.003048336],"category_scores_gemma":[0.007134208,0.0001220849,0.0001515101,0.0009313311,0.0002436063,0.001384239,0.0004541276,0.0003774184,0.0009996803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003325992,"about_ca_system_score_gemma":0.0002399901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001288845,"about_ca_topic_score_gemma":0.003229122,"domain_scores_codex":[0.9993477,0.0002691813,0.00004151492,0.0001460403,0.0001371552,0.00005842555],"domain_scores_gemma":[0.9953666,0.002593218,0.0007469756,0.0003050699,0.0008642604,0.0001239321],"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":[0.001441169,0.0002275465,0.2151147,0.0006836691,0.00008668083,0.00054867,0.03044036,0.0004777359,0.1589629,0.002575407,0.005656838,0.5837843],"study_design_scores_gemma":[0.00004726338,0.0009936296,0.8306241,0.0003580682,0.0001693604,0.001957619,0.04389057,0.01127575,0.07399523,0.005223469,0.03123815,0.0002268022],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580374,0.0005990138,0.0233951,0.0003782475,0.00008150654,0.0002169712,0.0008255305,0.0003119133,0.01615421],"genre_scores_gemma":[0.9649697,0.0005521827,0.02851771,0.0001566112,0.00004836371,0.00009979317,0.000346066,0.00005729558,0.005252256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003048336,"threshold_uncertainty_score":0.0101977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03575201494546505,"score_gpt":0.2791742121898664,"score_spread":0.2434221972444013,"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."}}