{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007147345,0.0001989457,0.0002284591,0.000161545,0.0001604584,0.00005598419,0.0005348555,0.00005691397,0.00007691905],"category_scores_gemma":[0.0002554283,0.0001096707,0.000162723,0.003477484,0.0001038071,0.00007116465,0.00008287532,0.0003081574,0.0001380605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000252031,"about_ca_system_score_gemma":0.00002924531,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0156963,"about_ca_topic_score_gemma":0.0004699965,"domain_scores_codex":[0.9983807,0.0001468234,0.0004022553,0.0003181599,0.0003034015,0.0004486217],"domain_scores_gemma":[0.9988425,0.0002446296,0.0002146467,0.000567307,0.00001607486,0.0001148787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001699911,0.0003597933,0.01010943,0.000005634527,0.00004470879,0.000002793956,0.001728747,0.03627492,0.007028367,0.00002213882,0.003469541,0.9407839],"study_design_scores_gemma":[0.0008064794,0.001108386,0.9603117,0.00005652801,0.00005379959,0.00002409098,0.001058463,0.02700119,0.003146668,0.002228269,0.003765152,0.0004392605],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935214,0.00005735856,0.0006332691,0.0007036416,0.0000339463,0.001170729,0.000006398109,0.0000317496,0.003841504],"genre_scores_gemma":[0.997629,0.00004614329,0.001875153,0.0002058977,0.00002325359,0.00002340262,0.000003704847,0.00001244754,0.0001809838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9502023,"threshold_uncertainty_score":0.9908583,"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."}}