{"id":"W1985338631","doi":"10.1080/01431160410001726030","title":"DSM generation and evaluation from QuickBird stereo imagery with 3D physical modelling","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Digital elevation model; Photogrammetry; Elevation (ballistics); Remote sensing; Lidar; Terrain; Land cover; Digital surface; Scale (ratio); Data set; Computer science; Geology; Geography; Artificial intelligence; Cartography; Land use; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002782317,0.0001065069,0.0001264223,0.00006526901,0.00008370951,0.0001068457,0.00009630842,0.00003514939,0.00001332522],"category_scores_gemma":[0.00002602457,0.00008830582,0.00004569101,0.00007913896,0.00008732673,0.0003105525,0.00003714876,0.0001622549,0.00001806468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002781436,"about_ca_system_score_gemma":0.00004328136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008354132,"about_ca_topic_score_gemma":0.00009990147,"domain_scores_codex":[0.9986508,0.00004722655,0.0002504706,0.0001801454,0.0007672859,0.0001041018],"domain_scores_gemma":[0.9993392,0.00004102413,0.0002686261,0.0001094952,0.0001703113,0.00007134683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000484344,0.00002775573,0.00005704741,6.851992e-7,0.00005419914,0.00003038389,0.001174791,0.3546152,0.07187078,0.00001270408,0.0000356309,0.5720724],"study_design_scores_gemma":[0.0008201972,0.00005107855,0.000821537,0.00008707886,0.00006260043,0.0003713678,0.0001040324,0.9827377,0.009932861,0.004473879,0.0004129247,0.0001247546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6363122,0.00002117508,0.3622805,0.0006645728,0.0001401543,0.00005052108,0.000001151983,0.000007307294,0.0005224069],"genre_scores_gemma":[0.7811539,0.00001875833,0.2181555,0.0001221236,0.0005209469,1.187871e-8,0.000006860652,0.00001153601,0.00001027725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6281225,"threshold_uncertainty_score":0.3601007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02428163696854597,"score_gpt":0.2731325074385308,"score_spread":0.2488508704699848,"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."}}