{"id":"W2187207702","doi":"","title":"STRIP ADJUSTMENT USING CONJUGATE PLANAR AND LINEAR FEATURES IN OVERLAPPING STRIPS","year":2008,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lidar; STRIPS; Calibration; Inertial measurement unit; Global Positioning System; Ranging; Remote sensing; Computer science; Photogrammetry; Conjugate points; Covariance; Computer vision; Artificial intelligence; Mathematics; Geography; Geometry; Statistics; Telecommunications","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":[0.0003769337,0.0004436101,0.0003295455,0.0004290541,0.0002214124,0.0003613478,0.0006495561,0.0002776493,0.001045387],"category_scores_gemma":[0.001343652,0.0002757895,0.0003867541,0.0005770125,0.0002871929,0.0004630843,0.0005377572,0.0003228982,0.0003334644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001852113,"about_ca_system_score_gemma":0.0003032829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001354846,"about_ca_topic_score_gemma":0.001056369,"domain_scores_codex":[0.999597,0.00005204607,0.00001859292,0.0001595407,0.0001267153,0.00004610183],"domain_scores_gemma":[0.9992342,0.000143402,0.0001192787,0.0003235185,0.0001474848,0.00003223972],"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":[0.0007177431,0.0003310319,0.008042858,0.0001200603,0.00008258312,0.0002760444,0.0004919274,0.1107895,0.5597407,0.003190165,0.001166233,0.3150512],"study_design_scores_gemma":[0.00005017108,0.0005698859,0.01465435,0.000009219706,0.00005733456,0.0003750196,0.0001338005,0.6887121,0.2891551,0.001154554,0.005058269,0.00007028986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4427433,0.00008207715,0.5539982,0.00003637514,0.00004429192,0.00006128365,0.00008127595,0.001520957,0.001432182],"genre_scores_gemma":[0.7474138,0.0000421934,0.2510011,0.00001929365,0.000007790151,0.00003289038,0.0002039111,0.0001511239,0.001128074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001354846,"threshold_uncertainty_score":0.003497124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247442845828947,"score_gpt":0.2440080931078202,"score_spread":0.2215336646495307,"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."}}