{"id":"W7119075833","doi":"10.1109/hfia68651.2025.00007","title":"Evaluating an Immersive Analytics Application at an Enterprise Business Intelligence Customer Conference","year":2025,"lang":"","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Usability; Novelty; Formative assessment; Perspective (graphical); Context (archaeology); Analytics; Business intelligence; Pluralistic walkthrough","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":[0.01342309,0.001156898,0.0005485135,0.001257963,0.001641824,0.005346022,0.002210712,0.001477284,0.006442414],"category_scores_gemma":[0.02736579,0.0004286665,0.0005474359,0.0009615569,0.001467911,0.00361574,0.003724181,0.001354382,0.0009800801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101764,"about_ca_system_score_gemma":0.001334409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001717881,"about_ca_topic_score_gemma":0.003406007,"domain_scores_codex":[0.9891025,0.007314742,0.0003373308,0.0005536751,0.002000958,0.000690751],"domain_scores_gemma":[0.9710544,0.01961677,0.0006741532,0.001387305,0.005621495,0.001645874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00420688,0.009708685,0.01839792,0.008928527,0.0003868981,0.003923664,0.1918358,0.01910477,0.2122232,0.01377706,0.02056159,0.4969451],"study_design_scores_gemma":[0.00165757,0.05032008,0.08130663,0.004180211,0.0008363181,0.003833611,0.1791838,0.1090633,0.2220127,0.01488651,0.3312441,0.001475236],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8912597,0.0007950508,0.07814407,0.001553397,0.0003086634,0.002443581,0.0004939792,0.001520801,0.02348063],"genre_scores_gemma":[0.8577876,0.0007548191,0.1293857,0.0007335526,0.0001308909,0.001803658,0.0007674681,0.0004884411,0.008147795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01342309,"threshold_uncertainty_score":0.07098889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08709432832887225,"score_gpt":0.4167100476550523,"score_spread":0.3296157193261801,"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."}}