{"id":"W2286284940","doi":"10.1016/j.ins.2016.01.004","title":"Vehicle detection from highway satellite images via transfer learning","year":2016,"lang":"en","type":"article","venue":"Information Sciences","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Transfer of learning; Detector; Artificial intelligence; Satellite; Aerial image; Computer vision; Domain (mathematical analysis); Key (lock); Coding (social sciences); Image (mathematics); Pattern recognition (psychology); Remote sensing; Real-time computing; Telecommunications","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":[0.000766711,0.0007237683,0.001085864,0.001171894,0.0002662935,0.0005353313,0.001262358,0.001218432,0.001006145],"category_scores_gemma":[0.002412135,0.0003722328,0.0006978655,0.000924856,0.0005926797,0.001498201,0.001182977,0.001177497,0.0006488721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004451263,"about_ca_system_score_gemma":0.0005821463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003594723,"about_ca_topic_score_gemma":0.003273543,"domain_scores_codex":[0.9996642,0.00008501711,0.00001351665,0.0001177557,0.00007147765,0.0000481356],"domain_scores_gemma":[0.9991398,0.0004109124,0.00007910314,0.0001684228,0.0001619859,0.00003982095],"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":[0.0003566799,0.0003078461,0.002313157,0.0002061071,0.0002192016,0.0001499195,0.0001070301,0.3088263,0.03461742,0.0040694,0.00436169,0.6444652],"study_design_scores_gemma":[0.000005247799,0.00002902307,0.0006387439,0.000003958384,0.00001112006,0.0000290182,0.00001241426,0.9906557,0.004447669,0.003884396,0.000276358,0.000006275321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07717384,0.0005589364,0.9193128,0.0002406766,0.00006275819,0.0000535281,0.0001596569,0.001323731,0.00111396],"genre_scores_gemma":[0.8244408,0.0005086719,0.1686291,0.0002270863,0.0001354285,0.00009739924,0.0009358222,0.0001180863,0.004907669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003594723,"threshold_uncertainty_score":0.00714761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01523124286481185,"score_gpt":0.2300002545770082,"score_spread":0.2147690117121964,"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."}}