{"id":"W1968014753","doi":"10.1023/b:supe.0000020178.66165.f3","title":"Optimization of DTM Interpolation Using SFS with Single Satellite Imagery","year":2004,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Interpolation (computer graphics); Terrain; Remote sensing; Computation; Digital elevation model; Satellite; Context (archaeology); Computer vision; Artificial intelligence; Field (mathematics); Elevation (ballistics); Computer graphics (images); Algorithm; Geology; Image (mathematics); Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004213988,0.00006052116,0.0000977087,0.00003586544,0.0001075552,0.00001754768,0.0001115653,0.00001997609,0.00001880786],"category_scores_gemma":[0.00001727371,0.00003861049,0.00003346627,0.000205229,0.0001105407,0.000189931,0.000035181,0.0001041618,0.000003209651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009438134,"about_ca_system_score_gemma":0.00001509875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001759285,"about_ca_topic_score_gemma":0.000007228477,"domain_scores_codex":[0.9993463,0.00004610061,0.0002734204,0.00005462567,0.0001866977,0.00009289356],"domain_scores_gemma":[0.9995562,0.00005413292,0.0002237817,0.0001034709,0.00003367093,0.00002870773],"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":[0.00001464156,0.00002957624,0.001202859,0.000002286726,0.000006776352,9.027843e-7,0.001506355,0.8649669,0.1269114,0.000006213857,0.000002805937,0.00534933],"study_design_scores_gemma":[0.001668493,0.0006547268,0.02706835,0.0009501924,0.0002751935,0.002223271,0.005158202,0.85799,0.102227,0.001096446,0.0002310999,0.0004569291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6545385,0.00003315625,0.3445921,0.0001086666,0.00002518284,0.00003417792,1.389039e-7,0.00000426715,0.0006638526],"genre_scores_gemma":[0.8626996,0.000009407901,0.1372039,0.00002849933,0.00004734523,1.095513e-8,4.513967e-7,0.000007536145,0.000003302497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2081611,"threshold_uncertainty_score":0.157449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159004112065297,"score_gpt":0.2238392353639511,"score_spread":0.2079388241574213,"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."}}