{"id":"W2106741906","doi":"10.5194/isprsarchives-xl-1-101-2014","title":"Calibration of multi-camera photogrammetric systems","year":2014,"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":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Photogrammetry; Orientation (vector space); Calibration; A priori and a posteriori; Computer science; Computer vision; Camera resectioning; Artificial intelligence; Stability (learning theory); Remote sensing; Geography; Mathematics","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":[0.002532485,0.0008038511,0.0005398701,0.001555446,0.0007221038,0.001308718,0.0007873399,0.0009927592,0.002723213],"category_scores_gemma":[0.007133202,0.0005884288,0.0004760539,0.001439974,0.0005512423,0.001369971,0.001551531,0.0009168159,0.001284262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009838284,"about_ca_system_score_gemma":0.0009008663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001449977,"about_ca_topic_score_gemma":0.001531145,"domain_scores_codex":[0.9967256,0.0007290185,0.0001578545,0.0006777248,0.001604405,0.0001053271],"domain_scores_gemma":[0.9973058,0.000656787,0.0003712886,0.0005543702,0.00106337,0.00004842703],"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":[0.0003964511,0.0001221337,0.009605666,0.0005777446,0.0001626024,0.0001941264,0.0006376631,0.2444169,0.1380477,0.01010067,0.00210016,0.5936381],"study_design_scores_gemma":[0.00005421005,0.0003779878,0.02136111,0.000191647,0.0000967805,0.0006400075,0.0002819938,0.7242992,0.2242557,0.006669286,0.02162682,0.0001452727],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03014947,0.000218767,0.9651181,0.00005670611,0.0000623982,0.000127279,0.00008089001,0.0007232222,0.003463286],"genre_scores_gemma":[0.5576929,0.0003351091,0.4383165,0.00007959318,0.00002701403,0.0002099524,0.0003109947,0.0001819477,0.002846061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002723213,"threshold_uncertainty_score":0.01339322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222365675592573,"score_gpt":0.2390161511617697,"score_spread":0.216792494405844,"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."}}