{"id":"W2109766168","doi":"10.1111/j.1477-9730.2009.00529.x","title":"A strip adjustment procedure to mitigate the impact of inaccurate mounting parameters in parallel lidar strips","year":2009,"lang":"en","type":"article","venue":"The Photogrammetric Record","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"STRIPS; Lidar; Computer science; Photogrammetry; Calibration; Robustness (evolution); Global Positioning System; Orientation (vector space); Remote sensing; Computer vision; Algorithm; Geology; Mathematics; Geometry; Telecommunications; Statistics","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":[0.0004045922,0.0005842452,0.0003780181,0.0005862813,0.0003378802,0.0003557795,0.0007047108,0.0003451066,0.001503955],"category_scores_gemma":[0.001244839,0.0003365042,0.000470738,0.0005735226,0.0002461936,0.000397963,0.0007913464,0.0003982416,0.0006184646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001121191,"about_ca_system_score_gemma":0.0004937928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001615282,"about_ca_topic_score_gemma":0.001626989,"domain_scores_codex":[0.9995285,0.0000936775,0.00003253391,0.000139827,0.0001598674,0.00004561094],"domain_scores_gemma":[0.999234,0.0001248224,0.0001473823,0.000254426,0.000213043,0.00002632649],"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":[0.0004149817,0.0001729152,0.008147173,0.0001519763,0.0001238473,0.0003621485,0.0003839923,0.06994671,0.3845614,0.002417841,0.0023019,0.531015],"study_design_scores_gemma":[0.0000707227,0.000643664,0.02119355,0.00001933492,0.0001508893,0.0007989242,0.0001632986,0.8016319,0.1620956,0.001362505,0.01177965,0.00008996688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1198354,0.00007133186,0.8780482,0.00002910317,0.00004791726,0.00006668781,0.00005445252,0.001003313,0.0008436141],"genre_scores_gemma":[0.5198388,0.00006562946,0.4777932,0.00003350775,0.00002580234,0.00006048191,0.0002691896,0.0001944593,0.001718997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001615282,"threshold_uncertainty_score":0.005031168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864926694307126,"score_gpt":0.2737862753377535,"score_spread":0.2551370083946822,"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."}}