{"id":"W2990234141","doi":"10.5194/isprs-archives-xlii-2-w18-99-2019","title":"INVESTIGATION INTO THE BEHAVIOUR AND MODELLING OF CHROMATIC ABERRATIONS IN NON-METRIC DIGITAL CAMERAS","year":2019,"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":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Curtin Institute for Computation, Curtin University of Technology; Natural Sciences and Engineering Research Council of Canada; Curtin University of Technology; University of Calgary","keywords":"Chromatic aberration; Chromatic scale; Orientation (vector space); Optics; Photogrammetry; Pixel; Artificial intelligence; Computer vision; Digital camera; Position (finance); Mathematics; Computer science; Physics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009305523,0.0004223378,0.0002292224,0.0007049523,0.0002440673,0.0007273995,0.0006388638,0.0005452761,0.0007068837],"category_scores_gemma":[0.005276491,0.0003458587,0.0004729034,0.0009126859,0.0003775359,0.0006656294,0.0003377082,0.0004324192,0.0001353446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467466,"about_ca_system_score_gemma":0.0005650207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.015276,"about_ca_topic_score_gemma":0.01622802,"domain_scores_codex":[0.9990932,0.0001696976,0.00004225055,0.0002008301,0.0004325683,0.0000614909],"domain_scores_gemma":[0.9974194,0.001418929,0.0004028151,0.0002916731,0.0004183399,0.00004888685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002669235,0.0001162222,0.0566991,0.0002875268,0.000138476,0.0002376031,0.0004349675,0.7893371,0.08898713,0.002770148,0.0003720373,0.06035276],"study_design_scores_gemma":[0.000008785796,0.0000777046,0.03643465,0.00001347725,0.00001897359,0.0001277785,0.00004994101,0.9420731,0.02026372,0.0002615694,0.0006408964,0.00002940661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8081767,0.0002735796,0.1881769,0.00008171675,0.00001947394,0.00008374033,0.0002101378,0.0005734091,0.002404314],"genre_scores_gemma":[0.9711761,0.00007322352,0.0280431,0.000009882394,0.000001819861,0.00001446388,0.0001302592,0.00004733409,0.0005037167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.015276,"threshold_uncertainty_score":0.03037411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907867285764175,"score_gpt":0.2263902710067877,"score_spread":0.207311598149146,"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."}}