{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001119383,0.001314623,0.0007782436,0.001061867,0.0005135747,0.00204958,0.001799758,0.001023502,0.003825303],"category_scores_gemma":[0.004268934,0.0005667695,0.0007636492,0.00109015,0.0006792545,0.002117507,0.001963483,0.00121076,0.002475055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005292436,"about_ca_system_score_gemma":0.0006865076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001540426,"about_ca_topic_score_gemma":0.003276329,"domain_scores_codex":[0.9991756,0.0001656986,0.00005192451,0.0002025553,0.0003415034,0.00006271145],"domain_scores_gemma":[0.9980988,0.0006666564,0.0001613436,0.0006198543,0.0003413261,0.0001119705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003323012,0.0003220823,0.003177713,0.0006940056,0.0001353034,0.0004258332,0.0007338795,0.118568,0.149775,0.01043285,0.0299263,0.6854766],"study_design_scores_gemma":[0.0001098731,0.0002655405,0.002068365,0.00006250207,0.00007550017,0.0007138053,0.0003618562,0.8077248,0.1170028,0.01075383,0.06070964,0.0001515773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02099326,0.0005280945,0.9591342,0.0001181639,0.00006544857,0.0001092605,0.0003120096,0.01539782,0.003341786],"genre_scores_gemma":[0.1653233,0.0004410861,0.8213087,0.0001869716,0.00004995546,0.0002334773,0.001283458,0.004601369,0.00657159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003825303,"threshold_uncertainty_score":0.01279694,"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."}}