{"id":"W2317615251","doi":"10.1177/154193120104501701","title":"Designing for Spatial Orientation in Endoscopic Environments","year":2001,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Orientation (vector space); Computer science; Computer vision; Workload; Perspective (graphical); Artificial intelligence; Colonoscopy; Navigational aid; Spatial analysis; Human–computer interaction; Remote sensing; Engineering; Medicine; Geography; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001421897,0.0000932392,0.0001052883,0.00002134384,0.0001361583,0.00002326332,0.00006552652,0.00005785596,0.000002641698],"category_scores_gemma":[0.00002471712,0.00008332267,0.00006061394,0.00005693036,0.00003221403,0.0001763635,0.00002542573,0.00008192878,1.961749e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006322603,"about_ca_system_score_gemma":0.000002829043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005199439,"about_ca_topic_score_gemma":0.00001210173,"domain_scores_codex":[0.9995047,0.000002139982,0.0001955675,0.0001076332,0.00005908644,0.0001308611],"domain_scores_gemma":[0.9998203,0.00003308161,0.00007977303,0.00002191963,0.00002118348,0.00002374599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004272739,0.0000442427,0.6295742,0.0002477088,0.00005430183,3.650499e-8,0.02108064,0.001713624,0.3441975,0.0003815401,0.0002043492,0.002459114],"study_design_scores_gemma":[0.002476781,0.0001536662,0.4452707,0.0004089058,0.00007571808,0.000001149889,0.02063668,0.0224444,0.5053089,0.001837248,0.0008783638,0.000507482],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987782,0.00002699131,0.0006944326,0.000009059733,0.00009292022,0.0002018742,0.000008885759,0.00002256765,0.0001650685],"genre_scores_gemma":[0.9989496,0.00005536391,0.0008692957,0.0000146651,0.00005683242,0.00001597535,0.000007651311,0.00001490177,0.00001564585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1843036,"threshold_uncertainty_score":0.33978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01478745414743493,"score_gpt":0.2243345938221066,"score_spread":0.2095471396746717,"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."}}