{"id":"W2889195092","doi":"10.1109/icce-china.2018.8448581","title":"Reading Behavior Analysis with Gaze Tracking Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gaze; Computer science; Reading (process); Human–computer interaction; Eye tracking; Tracking (education); Artificial intelligence; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002442953,0.0001025206,0.0001616387,0.0002621287,0.0001407057,0.0001451721,0.001750779,0.00004897495,0.00005387996],"category_scores_gemma":[0.00002518766,0.00007496115,0.00002864404,0.001422692,0.0001304525,0.0004727388,0.0004143485,0.0001034119,0.00006893407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001573795,"about_ca_system_score_gemma":0.00002427114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008349101,"about_ca_topic_score_gemma":0.0001916079,"domain_scores_codex":[0.9988708,0.00002189154,0.0001297805,0.0005604922,0.0001670781,0.0002499037],"domain_scores_gemma":[0.998071,0.00003795707,0.00006123626,0.001681813,0.00009954038,0.00004852728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001079846,0.0003003783,0.4442029,0.000006588976,0.0006577051,0.0001793984,0.0004244375,0.00001446562,0.003808869,0.1163071,0.002242272,0.4318451],"study_design_scores_gemma":[0.0006030686,0.000552372,0.8540154,0.00004040394,0.001050912,0.0001238445,0.0001622392,0.1120408,0.02315415,0.0005743592,0.006834105,0.0008483445],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0936543,0.000007242379,0.9002672,0.0004113613,0.00006238833,0.00004928396,0.000002957036,0.0006047448,0.004940456],"genre_scores_gemma":[0.8672076,9.776148e-7,0.1323122,0.00008782555,0.00003751598,0.000004134391,0.000007549465,0.000005002096,0.0003372077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7735533,"threshold_uncertainty_score":0.3253411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05416828771077315,"score_gpt":0.3106362742262125,"score_spread":0.2564679865154393,"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."}}