{"id":"W4414015845","doi":"10.11159/icbes25.203","title":"Spatial Language in Augmented Reality: An XR Framework for Investigating Visuospatial Cognition","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Augmented reality; Cognition; Computer science; Spatial cognition; Human–computer interaction; Cognitive psychology; Virtual reality; Mixed reality; Psychology; Neuroscience","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005618556,0.0001181232,0.0001855471,0.000296172,0.0001833774,0.0002814128,0.0005770452,0.00004227781,5.753813e-8],"category_scores_gemma":[0.0001406746,0.00009388285,0.00002218144,0.0013629,0.0001196134,0.0002426245,0.00019405,0.0001815961,5.501994e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004580145,"about_ca_system_score_gemma":0.00004607867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001034402,"about_ca_topic_score_gemma":0.00001048465,"domain_scores_codex":[0.9988568,0.000009842933,0.0002622115,0.0003971476,0.0002332423,0.000240721],"domain_scores_gemma":[0.9993443,0.0001838102,0.0001169581,0.0001450561,0.000128363,0.00008146327],"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":[0.000009557254,0.00006706871,0.0008251237,0.0001842768,0.00001288842,2.409082e-7,0.0003092925,0.002797172,0.00572937,0.9664397,0.00006750126,0.02355787],"study_design_scores_gemma":[0.0002200892,0.00007664077,0.003846432,0.0004815792,0.000006323789,0.000003014997,0.00001417891,0.9893492,0.003928528,0.001907224,0.00006801402,0.00009869975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1571332,0.0001041483,0.8402984,0.0009924348,0.0005974885,0.0006510438,0.000003320861,0.0001033336,0.0001165882],"genre_scores_gemma":[0.9911775,0.000003751666,0.008531502,0.000105552,0.00006543157,0.00007792176,4.470047e-7,0.00000427916,0.00003360112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9865521,"threshold_uncertainty_score":0.3828431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01130418092636322,"score_gpt":0.2644377998527658,"score_spread":0.2531336189264026,"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."}}