{"id":"W4390061979","doi":"10.3390/rs16010051","title":"Enhancing Object Detection in Remote Sensing: A Hybrid YOLOv7 and Transformer Approach with Automatic Model Selection","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object detection; Artificial intelligence; Field (mathematics); Bridging (networking); Data mining; Process (computing); Remote sensing; Computer vision; Real-time computing; Pattern recognition (psychology); Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000847223,0.0007512029,0.0009725258,0.001135291,0.0002795143,0.001063347,0.001521128,0.0007834425,0.001790681],"category_scores_gemma":[0.001094847,0.0004542915,0.001122116,0.0006447113,0.0004222166,0.0009932237,0.001343537,0.0006890256,0.0009855129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004436152,"about_ca_system_score_gemma":0.0006572865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002693414,"about_ca_topic_score_gemma":0.003519058,"domain_scores_codex":[0.9994171,0.0001081502,0.00002374801,0.0001253124,0.0002457802,0.00007993604],"domain_scores_gemma":[0.9995626,0.000159169,0.00004633048,0.00007093936,0.0001280182,0.00003295773],"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":[0.0003633399,0.000287877,0.002722592,0.0001424744,0.0001974966,0.0002086135,0.0001254315,0.1967231,0.1076135,0.008843241,0.002029129,0.6807432],"study_design_scores_gemma":[0.000008533772,0.00005426302,0.0003304064,0.000003142946,0.00001919832,0.00006979422,0.00001385099,0.9878533,0.009404915,0.001500341,0.0007329306,0.000009322203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01534516,0.00009365311,0.9829901,0.00005003114,0.00001266848,0.0000274647,0.00001838783,0.0007581613,0.0007041912],"genre_scores_gemma":[0.4812619,0.0002587994,0.513424,0.0002492121,0.00005649247,0.00009238291,0.0003102631,0.0003549808,0.003991992],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002693414,"threshold_uncertainty_score":0.005990386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01484790882954276,"score_gpt":0.2193130859036894,"score_spread":0.2044651770741467,"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."}}