{"id":"W4224298125","doi":"10.1109/vr51125.2022.00032","title":"Effects of Field of View on Dynamic Out-of-View Target Search in Virtual Reality","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Workload; Visual search; Field of view; Computer vision; Computer science; Virtual reality; Movement (music); Target acquisition; Artificial intelligence; Trajectory; Field (mathematics); Eye movement; Mathematics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001622271,0.0003359588,0.0008533268,0.0002996747,0.0001454225,0.00006140345,0.001396552,0.0001411181,0.0001483036],"category_scores_gemma":[0.0003089742,0.0003173057,0.0001310393,0.0007278732,0.0002621557,0.0003105884,0.0007063081,0.0007350949,0.000009814497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00012754,"about_ca_system_score_gemma":0.0002848979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001195619,"about_ca_topic_score_gemma":0.0003232307,"domain_scores_codex":[0.9959643,0.0009673766,0.0009801175,0.0007644509,0.0008908516,0.0004329009],"domain_scores_gemma":[0.9969916,0.001174396,0.0004281352,0.00105267,0.0001672573,0.0001859028],"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":[0.001497496,0.004299991,0.0003924797,0.001798098,0.000262663,0.00003238262,0.01990036,0.008625798,0.05146899,0.3844765,0.001616487,0.5256287],"study_design_scores_gemma":[0.007509296,0.05247049,0.02177728,0.004745981,0.0002139286,0.00002690358,0.007642808,0.3284288,0.5254415,0.02731922,0.02103537,0.00338847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9257488,0.0004557863,0.06528055,0.002067392,0.0007084713,0.001336304,0.0004729854,0.00007162757,0.003858057],"genre_scores_gemma":[0.9981434,0.0009502291,0.0001501828,0.0003609108,0.00001290503,0.00009240843,0.00002219398,0.0000153034,0.0002524185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5222403,"threshold_uncertainty_score":0.9999279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04899717463092317,"score_gpt":0.3395681496031405,"score_spread":0.2905709749722173,"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."}}