{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004072802,0.0008350008,0.0006457727,0.001166774,0.0009296538,0.004164081,0.001426477,0.0009493613,0.006150473],"category_scores_gemma":[0.01654986,0.0004442392,0.000585905,0.00109521,0.001740788,0.006377089,0.007724394,0.0009716581,0.001015228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003411065,"about_ca_system_score_gemma":0.0007089089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008571706,"about_ca_topic_score_gemma":0.002522052,"domain_scores_codex":[0.9969001,0.001757511,0.0001539485,0.0002916981,0.000659506,0.0002372229],"domain_scores_gemma":[0.9887517,0.007199618,0.0005205797,0.002461636,0.0005527472,0.0005135902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003622365,0.0007542234,0.0213922,0.004391078,0.0003051217,0.001699857,0.06276321,0.007575452,0.1124064,0.04961132,0.03509206,0.7003868],"study_design_scores_gemma":[0.0005687016,0.002879741,0.05170382,0.002596221,0.0005499194,0.006293671,0.04915413,0.1765752,0.1244738,0.1623726,0.4217674,0.001064758],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2240052,0.001749816,0.7187585,0.001974654,0.0001586756,0.001339636,0.002247082,0.02087325,0.02889305],"genre_scores_gemma":[0.6146296,0.0008745629,0.3748989,0.0004729533,0.00007196912,0.0008347597,0.001310211,0.001224722,0.005682356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006150473,"threshold_uncertainty_score":0.02153927,"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."}}