{"id":"W4231166150","doi":"10.5194/isprsarchives-xli-b5-99-2016","title":"EXPERIMENTS ON CALIBRATING TILT-SHIFT LENSES FOR CLOSE-RANGE PHOTOGRAMMETRY","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Kessler Foundation","keywords":"Photogrammetry; Lens (geology); Optics; Distortion (music); Tilt (camera); Optical axis; Pinhole (optics); Scheimpflug principle; Focus (optics); Computer science; Aperture (computer memory); Camera lens; Metrology; Computer vision; Physics; Engineering; Acoustics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001532303,0.000766288,0.0006208527,0.0008051597,0.0005039182,0.0005357685,0.001210382,0.001109172,0.001435648],"category_scores_gemma":[0.004241702,0.0003966752,0.0003749302,0.001230504,0.0006937039,0.0008485973,0.0008271979,0.0005574445,0.000446403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005878006,"about_ca_system_score_gemma":0.0003599535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001858907,"about_ca_topic_score_gemma":0.001881132,"domain_scores_codex":[0.9972639,0.0004319737,0.000128167,0.0005881859,0.001337703,0.0002501002],"domain_scores_gemma":[0.9968871,0.001135622,0.0003334986,0.00065242,0.0008486324,0.0001426819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004945548,0.0002188459,0.003445674,0.0003207785,0.00003502353,0.0001796745,0.0005523151,0.004063531,0.9545176,0.0008602795,0.0001469314,0.0351648],"study_design_scores_gemma":[0.00004330779,0.001329375,0.009420472,0.00002182684,0.00004468679,0.0002611986,0.0002098739,0.008270479,0.9785565,0.0001953956,0.001606679,0.00004026403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8778249,0.0009843352,0.1175301,0.00009214714,0.00009937547,0.0002818538,0.000201808,0.0004686831,0.002516781],"genre_scores_gemma":[0.928396,0.0004064291,0.06990119,0.00002945846,0.00001459232,0.00006654336,0.0001381869,0.00005372092,0.000993832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001858907,"threshold_uncertainty_score":0.008103669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03489215886260078,"score_gpt":0.2828305369999487,"score_spread":0.2479383781373479,"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."}}