{"id":"W4404926798","doi":"10.1007/978-3-031-78456-9_11","title":"An Empirical Evaluation of the Impact of Solar Correction in NeRFs for Satellite Imagery","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Satellite; Remote sensing; Geology; Astronomy; Physics","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":[0.008475721,0.000495107,0.0002768269,0.000884049,0.0004861275,0.001134027,0.001451585,0.0009277636,0.005849123],"category_scores_gemma":[0.07558829,0.0002308787,0.0006080681,0.001731845,0.0009324588,0.002199543,0.0008021554,0.0008282796,0.0008557596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001388201,"about_ca_system_score_gemma":0.0007823846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02525621,"about_ca_topic_score_gemma":0.02622711,"domain_scores_codex":[0.9940755,0.003603292,0.0002035944,0.0006334934,0.001284832,0.0001992483],"domain_scores_gemma":[0.7990729,0.1806868,0.005441756,0.006028607,0.007875813,0.0008942066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004690568,0.002027735,0.6989768,0.0004681374,0.0006122513,0.0004904157,0.0008923612,0.1447721,0.00401873,0.002061815,0.00322671,0.1377625],"study_design_scores_gemma":[0.0001893306,0.003056251,0.8109106,0.0001014976,0.0004877319,0.0004094186,0.002454654,0.1718735,0.005222803,0.001178158,0.004057072,0.0000589884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883129,0.0004978758,0.003804922,0.0002186639,0.00002432724,0.00006333362,0.000942343,0.0001091287,0.006026527],"genre_scores_gemma":[0.994886,0.0001101725,0.002984354,0.00003048009,0.00001488307,0.00001916554,0.0008481316,0.00003007825,0.001076872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02525621,"threshold_uncertainty_score":0.0502184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03047868556314203,"score_gpt":0.3285849794382271,"score_spread":0.2981062938750851,"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."}}