{"id":"W2894399415","doi":"10.5194/isprs-archives-xlii-1-107-2018","title":"UAV-LiCAM SYSTEM DEVELOPMENT: CALIBRATION AND GEO-REFERENCING","year":2018,"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":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Géomatique du Québec; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"GNSS applications; Computer science; Inertial measurement unit; Lidar; Remote sensing; Real-time computing; Calibration; Inertial navigation system; Orientation (vector space); Computer vision; Global Positioning System; Geography; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009907708,0.0005932997,0.0004932722,0.0008849647,0.0003036666,0.0008044664,0.0009806242,0.0004841723,0.006919915],"category_scores_gemma":[0.001654291,0.000191586,0.0002610277,0.0004800634,0.000167306,0.0005379097,0.0009295103,0.0005114417,0.004615558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000383433,"about_ca_system_score_gemma":0.0006725544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001100306,"about_ca_topic_score_gemma":0.0009357392,"domain_scores_codex":[0.9993636,0.0001254903,0.00003614232,0.0001083262,0.0003200092,0.00004654404],"domain_scores_gemma":[0.9993398,0.0000519698,0.00006497632,0.0001791048,0.0003258727,0.00003838567],"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":[0.0003243695,0.0001738211,0.007723946,0.0004606366,0.00009118834,0.000486332,0.0003530064,0.01838157,0.1040565,0.004663427,0.02296395,0.8403214],"study_design_scores_gemma":[0.000245219,0.001144275,0.02588494,0.0001766005,0.00009922984,0.001414999,0.0002163311,0.5023816,0.2598769,0.001797657,0.2066503,0.0001118717],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05623057,0.0004580808,0.8957744,0.0001889123,0.000365536,0.0007297481,0.0008496511,0.02988852,0.01551463],"genre_scores_gemma":[0.4696675,0.0002173282,0.5124274,0.0002616151,0.0001238133,0.000993653,0.003892602,0.001042239,0.0113738],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006919915,"threshold_uncertainty_score":0.02314943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02093891454772181,"score_gpt":0.2327579855698547,"score_spread":0.2118190710221329,"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."}}