{"id":"W4393167313","doi":"10.1109/mcg.2024.3353888","title":"Databiting: Lightweight, Transient, and Insight Rich Exploration of Personal Data","year":2024,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea","keywords":"Computer science; Personalization; Modalities; Human–computer interaction; Wearable computer; Focus (optics); Data science; Data visualization; Data exploration; Wearable technology; Visualization; Multimedia; World Wide Web; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001946894,0.0001160217,0.0001281553,0.0003292894,0.0001668508,0.0001939583,0.0005235915,0.00006773845,0.000001636708],"category_scores_gemma":[0.000002154408,0.0001084213,0.00001748103,0.0007084371,0.0001565231,0.001355006,0.0002336288,0.0002174851,0.000004574319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007046068,"about_ca_system_score_gemma":0.00002866162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000543037,"about_ca_topic_score_gemma":0.000009293579,"domain_scores_codex":[0.9989477,0.00002444474,0.0002388542,0.0005417723,0.0001284891,0.0001186772],"domain_scores_gemma":[0.9991052,0.0000994577,0.00006759702,0.0005779063,0.0001168072,0.00003308052],"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":[0.000001064353,0.00004907976,0.00006289094,0.0000663027,0.00003890891,0.0000020409,0.001051231,0.000001445474,0.001966895,0.9600747,0.002017053,0.03466845],"study_design_scores_gemma":[0.00025125,0.0001079939,0.001288401,0.0001104353,0.00003907762,0.00008166943,0.00004551226,0.7283408,0.003196779,0.03880228,0.2274205,0.000315247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01482824,0.0005528131,0.9820748,0.001689542,0.0002105962,0.0002245219,0.00005308147,0.0001662909,0.0002001109],"genre_scores_gemma":[0.9778984,0.0003636034,0.02108583,0.0001990235,0.000247772,0.00007170785,0.0001087031,0.00001056291,0.00001436022],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9630702,"threshold_uncertainty_score":0.4421294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07092381398260478,"score_gpt":0.3074961868292395,"score_spread":0.2365723728466347,"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."}}