{"id":"W2547697372","doi":"10.1109/igarss.2016.7729396","title":"Object detection in pleiades images using deep features","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Effigis (Canada); Computer Research Institute of Montréal","funders":"","keywords":"Pleiades; Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Exploit; Object detection; Computer vision; Object (grammar); Deep neural networks; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology)","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":[0.00005302451,0.00006980795,0.0000618183,0.00007675512,0.00006412737,0.00003493706,0.0002836318,0.00002871109,0.000007328968],"category_scores_gemma":[0.00002316047,0.00004540264,0.00002096776,0.0003686944,0.00002591174,0.0004922324,0.000101786,0.00005012814,0.0000207646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005496365,"about_ca_system_score_gemma":0.000009069882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002658281,"about_ca_topic_score_gemma":0.000218199,"domain_scores_codex":[0.9993809,0.00002462911,0.00009722039,0.0002421385,0.00008242373,0.0001727232],"domain_scores_gemma":[0.9995177,0.0001147576,0.00003362099,0.0002811505,0.00002118555,0.00003153108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003670248,0.00001924652,0.0007811907,0.00000195886,0.00000242808,0.000003948933,0.00005658313,0.0006963523,0.4353867,0.01057346,0.00007607089,0.5523984],"study_design_scores_gemma":[0.0004567818,0.00004413795,0.05319495,0.00003110957,0.000002826303,0.00007163279,0.00001655601,0.03254385,0.8620276,0.05033781,0.0009284992,0.0003442258],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03352436,0.00006633538,0.9649438,0.0004679062,0.00006605691,0.00009767935,2.387576e-7,0.0001593258,0.0006743618],"genre_scores_gemma":[0.8816447,0.00001704316,0.1179568,0.0001017878,0.00004024508,0.00001411134,6.774659e-8,0.000004724237,0.0002204781],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8481204,"threshold_uncertainty_score":0.1851466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614770466482416,"score_gpt":0.2742768634363784,"score_spread":0.2581291587715542,"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."}}