{"id":"W2077519102","doi":"10.1117/12.2067420","title":"Synthetic aperture ladar concept for infrastructure monitoring","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National d'Optique","funders":"","keywords":"Synthetic aperture radar; Remote sensing; Interferometric synthetic aperture radar; Interferometry; Lidar; Radar imaging; Radar; Computer science; Inverse synthetic aperture radar; Geology; Optics; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003760393,0.0003850516,0.0004593652,0.00008943125,0.0001041671,0.00009200161,0.0009040607,0.0003040667,0.00001127944],"category_scores_gemma":[0.000555764,0.0003201657,0.0005635906,0.0002350181,0.0001887651,0.0002753094,0.00008608087,0.0003267819,0.000001014356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001517171,"about_ca_system_score_gemma":0.00001618771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002801155,"about_ca_topic_score_gemma":4.054719e-8,"domain_scores_codex":[0.9981667,1.377199e-8,0.0005939829,0.0003683814,0.000454526,0.000416347],"domain_scores_gemma":[0.9983282,0.0003858412,0.000194685,0.0001029799,0.0008675543,0.0001207448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003455352,0.00005761044,0.0001467122,0.000723726,0.0004377161,2.5188e-8,0.0002677979,0.0001659334,0.3789911,0.5947237,0.008363958,0.01608719],"study_design_scores_gemma":[0.001016016,0.0002796166,0.0004648624,0.0005550329,0.0002431425,0.00002477085,0.0009024533,0.1203194,0.4595627,0.006783586,0.4091045,0.0007440153],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9807474,0.0002607379,0.01207915,0.001556166,0.000386501,0.0009612782,0.0001039695,0.000447741,0.003457031],"genre_scores_gemma":[0.3664594,0.0001036406,0.6321122,0.00007267956,0.0007188562,0.0003216284,0.00001131993,0.0001161395,0.0000840812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6200331,"threshold_uncertainty_score":0.999925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006383685808981902,"score_gpt":0.2150884915422081,"score_spread":0.2087048057332262,"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."}}