{"id":"W2397993141","doi":"","title":"Pop-up Depth Views for Improving 3D Target Acquisition","year":2011,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Cursor (databases); Computer science; Computer vision; Target acquisition; Artificial intelligence; Depth perception; Perspective (graphical); Task (project management); Stereo display; 3D interaction; Stereopsis; Stereoscopy; Computer graphics (images); Virtual reality; Perception","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":[0.0007553444,0.002744855,0.001134065,0.001009317,0.0003642258,0.001686077,0.001182788,0.001490633,0.02883791],"category_scores_gemma":[0.004099059,0.001623224,0.001077601,0.001087189,0.0004340198,0.00194175,0.004399576,0.002266607,0.004557319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002553882,"about_ca_system_score_gemma":0.000492054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008490215,"about_ca_topic_score_gemma":0.001669265,"domain_scores_codex":[0.9989008,0.0001896191,0.00004580633,0.0001232116,0.0005897295,0.0001508457],"domain_scores_gemma":[0.9972268,0.001493233,0.0001006548,0.0006066911,0.0004573633,0.0001153102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001191202,0.0001525433,0.0006087604,0.000778269,0.0001080143,0.0003430422,0.0005383911,0.01299367,0.626649,0.003465118,0.0078394,0.3453327],"study_design_scores_gemma":[0.0002683181,0.0009581069,0.006054929,0.0002535104,0.0003177523,0.001942878,0.0004376317,0.2730226,0.6695957,0.004160882,0.04276209,0.0002255753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03854661,0.001523132,0.9449579,0.0002282674,0.0001725485,0.0002080493,0.0007038739,0.007553305,0.006106478],"genre_scores_gemma":[0.1860617,0.001918747,0.8004674,0.0002968138,0.0001195013,0.0002558912,0.001277026,0.003266149,0.006336893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02883791,"threshold_uncertainty_score":0.09647244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02591506438897925,"score_gpt":0.2562237495012274,"score_spread":0.2303086851122482,"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."}}