{"id":"W3186819486","doi":"10.3390/ijgi10070488","title":"Semantic Relation Model and Dataset for Remote Sensing Scene Understanding","year":2021,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Key Research and Development Program of Ningxia; National Natural Science Foundation of China","keywords":"Computer science; Graph; Artificial intelligence; Semantic gap; Context (archaeology); Image (mathematics); Theoretical computer science; Image retrieval","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":[0.0006254924,0.001565744,0.0006220746,0.003629968,0.0007443537,0.0009363577,0.002913599,0.001747883,0.004244762],"category_scores_gemma":[0.00251299,0.0003250442,0.001944743,0.004559945,0.0006675686,0.002537742,0.001612821,0.001861698,0.002258444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001612834,"about_ca_system_score_gemma":0.001197917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02176531,"about_ca_topic_score_gemma":0.03681848,"domain_scores_codex":[0.9991077,0.0001449988,0.00007583939,0.0003882028,0.000203533,0.0000797105],"domain_scores_gemma":[0.998992,0.0001421246,0.00009620706,0.0004827044,0.0002110506,0.00007585447],"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":[0.0009911699,0.002307581,0.02412563,0.00305932,0.0005857769,0.001532924,0.0006769371,0.1382618,0.03382934,0.03804225,0.3460803,0.4105071],"study_design_scores_gemma":[0.0003024812,0.0003756524,0.03701498,0.0002497083,0.0002988292,0.001309497,0.001291724,0.5933198,0.02366588,0.04248541,0.2994628,0.0002233443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1827456,0.00216079,0.2492826,0.002776513,0.0005729931,0.002005741,0.5038257,0.03598397,0.02064614],"genre_scores_gemma":[0.2014776,0.0005012018,0.1951403,0.000445738,0.00006987924,0.001128226,0.5984653,0.0004962563,0.002275538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02176531,"threshold_uncertainty_score":0.04327726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03930285075570208,"score_gpt":0.3207697049246217,"score_spread":0.2814668541689196,"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."}}