{"id":"W2004953068","doi":"10.5194/isprsarchives-xl-1-w1-41-2013","title":"DSMS GENERATION FROM COSMO-SKYMED, RADARSAT-2 AND TERRASAR-X IMAGERY ON BEAUPORT (CANADA) TEST SITE: EVALUATION AND COMPARISON OF DIFFERENT RADARGRAMMETRIC APPROACHES","year":2013,"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":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Deutsches Zentrum für Luft- und Raumfahrt","keywords":"Orientation (vector space); Software; Remote sensing; Computer science; Ground truth; GNSS applications; A priori and a posteriori; Artificial intelligence; Geology; Global Positioning System; Mathematics; Telecommunications","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":[0.0006329152,0.0007844652,0.0002946547,0.001726351,0.0004025192,0.0009683182,0.0006306523,0.0003840463,0.001629672],"category_scores_gemma":[0.0009558912,0.0001874713,0.000569758,0.001565898,0.0003151667,0.0003157266,0.0004450903,0.0002738574,0.0006113732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001473762,"about_ca_system_score_gemma":0.001849343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2207297,"about_ca_topic_score_gemma":0.281543,"domain_scores_codex":[0.9996424,0.00005401147,0.00001366787,0.0000575541,0.0001794017,0.00005294674],"domain_scores_gemma":[0.999536,0.00006386569,0.00002783197,0.00006573815,0.0002674649,0.00003898094],"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":[0.00156931,0.0003655195,0.0433597,0.0007245588,0.0004322676,0.0005365485,0.0006710577,0.5461875,0.0561366,0.001574801,0.008788253,0.3396539],"study_design_scores_gemma":[0.0001985425,0.0001961669,0.1059879,0.00007815866,0.0001507536,0.0002090254,0.0008687953,0.843147,0.04038053,0.0004736556,0.008198914,0.0001104824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9225749,0.000436193,0.05256477,0.0001869929,0.00009708211,0.0002795886,0.009518948,0.003677256,0.01066432],"genre_scores_gemma":[0.9298613,0.0002119931,0.05657741,0.00003118592,0.00001033799,0.00004868833,0.01112243,0.0003192067,0.001817501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7792703,"threshold_uncertainty_score":0.4388899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03239942902574357,"score_gpt":0.2517138666620184,"score_spread":0.2193144376362748,"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."}}