{"id":"W2418106460","doi":"10.5194/isprs-archives-xli-b3-481-2016","title":"CONTINUOUS MAPPING OF TUNNEL WALLS IN A GNSS-DENIED ENVIRONMENT","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mobile mapping; Computer science; Computer vision; GNSS applications; Global Positioning System; Point cloud; Inertial measurement unit; Artificial intelligence; Lidar; Remote sensing; Feature (linguistics); Geology","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":[0.000225357,0.00031819,0.0002846148,0.0004640044,0.0002649988,0.0003053804,0.0003888082,0.0002345928,0.0006601989],"category_scores_gemma":[0.0004751737,0.0001370584,0.0001702679,0.0003287187,0.0002968526,0.0002925698,0.0005086998,0.0003053629,0.0001870832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157949,"about_ca_system_score_gemma":0.0002690833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002720458,"about_ca_topic_score_gemma":0.004563828,"domain_scores_codex":[0.9998455,0.0000322153,0.00000377995,0.00002576678,0.00006283032,0.000029835],"domain_scores_gemma":[0.9996498,0.0000893387,0.00003306538,0.00007197267,0.00009112013,0.00006474814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002245594,0.0005190762,0.03738147,0.0003038888,0.000158022,0.002044107,0.001063313,0.3553064,0.4046322,0.00119235,0.001520434,0.1936333],"study_design_scores_gemma":[0.00008505498,0.0007319402,0.05761134,0.00003375219,0.00002536938,0.0004690224,0.0005288479,0.8780709,0.0597569,0.0007925573,0.001842254,0.00005212349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723591,0.00005073518,0.02558416,0.00002007231,0.00001563719,0.00002546055,0.0001717541,0.0004151615,0.001357961],"genre_scores_gemma":[0.9823483,0.00002388144,0.01676002,0.000004870965,0.000003657643,0.00001161285,0.0002276747,0.00001715077,0.0006028387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002720458,"threshold_uncertainty_score":0.0054093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878485246495979,"score_gpt":0.2221280461319075,"score_spread":0.2033431936669478,"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."}}