{"id":"W2078433651","doi":"10.1109/plans.2014.6851508","title":"Vision-based context and height estimation for 3D indoor location","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trusted Positioning (Canada); University of Calgary","funders":"","keywords":"GNSS applications; Computer science; Inertial navigation system; Inertial measurement unit; Context (archaeology); Mobile device; Accelerometer; Inertial frame of reference; Global Positioning System; Barometer; Real-time computing; Computer vision; Artificial intelligence; Telecommunications; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.00008639425,0.00007495652,0.00008096046,0.00007292055,0.00005185883,0.00003011875,0.00004456564,0.00007886584,0.00002027804],"category_scores_gemma":[0.0001170288,0.00006553904,0.00001317197,0.00008933222,0.00002695798,0.00008680407,0.00000571846,0.00003249972,0.00001446819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001872572,"about_ca_system_score_gemma":0.000005750467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004644515,"about_ca_topic_score_gemma":0.00001348787,"domain_scores_codex":[0.9996426,0.000005585054,0.0001183611,0.00008814935,0.00005182299,0.00009356004],"domain_scores_gemma":[0.9997051,0.00009593874,0.00001515845,0.0001047061,0.00005915498,0.00001989727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001670955,0.00001957432,0.0006161717,0.0002827236,0.00001431731,1.115433e-7,0.0001025276,0.1094219,0.0007363863,0.04889711,0.004898448,0.8349941],"study_design_scores_gemma":[0.0004246674,0.00004586221,0.0003636737,0.00001623789,0.000005340917,3.436378e-7,0.00002939781,0.9674631,0.02327888,0.001155525,0.007125826,0.00009111416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00796885,0.00006817105,0.9897103,0.0001885272,0.00009773189,0.00019564,0.000001953556,0.0006398016,0.001129072],"genre_scores_gemma":[0.9706959,0.000004006302,0.02898983,0.0001603146,0.00001793646,0.00004009196,0.00002430709,0.0000137018,0.0000538943],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9627271,"threshold_uncertainty_score":0.2672604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004896732297773826,"score_gpt":0.2134215523250839,"score_spread":0.2085248200273101,"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."}}