{"id":"W4403447917","doi":"10.1109/vl/hcc60511.2024.00032","title":"FlexDoc: Flexible Document Adaptation through Optimizing both Content and Layout","year":2024,"lang":"en","type":"article","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Engineering and Physical Sciences Research Council","keywords":"Adaptation (eye); Computer science; Content adaptation; Content (measure theory); Information retrieval; Human–computer interaction; Ubiquitous computing","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":[0.0001995127,0.00009208981,0.000107591,0.00006837351,0.00008282311,0.0007312907,0.0002267551,0.0000242501,0.00004043587],"category_scores_gemma":[0.000009664669,0.00007060385,0.00004312591,0.0002571901,0.00002099183,0.001090948,0.0001680898,0.00006423926,0.0000667904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002339024,"about_ca_system_score_gemma":0.00003539651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003522482,"about_ca_topic_score_gemma":0.00001337338,"domain_scores_codex":[0.99914,0.00002418572,0.0001524454,0.0003530848,0.0001709367,0.0001593872],"domain_scores_gemma":[0.9995922,0.00005479248,0.00002084947,0.0002621743,0.00002131748,0.00004859826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004940443,0.00003728432,0.00009585633,0.00006724612,0.0002014936,0.00005857132,0.008969123,0.003982733,0.001179971,0.7767221,0.01065275,0.1980279],"study_design_scores_gemma":[0.0001906212,0.00007871209,0.00008299787,0.0001007187,0.0000391148,0.00002051934,0.001313684,0.9628253,0.002000245,0.004142123,0.02897442,0.0002315759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002986959,0.001227408,0.9851395,0.002109554,0.0001957855,0.00004758323,0.000002296699,0.0003725758,0.00791834],"genre_scores_gemma":[0.6130496,0.0001593902,0.378967,0.0005176759,0.00005949466,0.00000964427,0.00001043416,0.00000746348,0.007219224],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9588425,"threshold_uncertainty_score":0.7051854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07299222884257507,"score_gpt":0.2870124514158426,"score_spread":0.2140202225732676,"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."}}