{"id":"W2909757149","doi":"10.4018/978-1-5225-1814-3.ch004","title":"Improving the Efficiency of Image Interpretation Using Ground Truth Terrestrial Photographs","year":2016,"lang":"en","type":"book-chapter","venue":"Advances in geospatial technologies book series","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Island University","funders":"","keywords":"Ground truth; Metadata; Remote sensing; Field (mathematics); Computer science; Interpretation (philosophy); Overhead (engineering); Satellite imagery; Satellite image; Computer vision; Artificial intelligence; Geography; Satellite; World Wide Web; Engineering; Mathematics","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":[0.002086183,0.0009455105,0.0009634505,0.003511647,0.000552092,0.00383117,0.002374651,0.0009247552,0.01699017],"category_scores_gemma":[0.007018017,0.0005915341,0.0006861911,0.004367609,0.0006179508,0.0043109,0.002195118,0.0007152079,0.01031126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004635516,"about_ca_system_score_gemma":0.0005533999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002349101,"about_ca_topic_score_gemma":0.003333545,"domain_scores_codex":[0.9981036,0.0003998861,0.00008020874,0.0003729325,0.0009558424,0.00008755912],"domain_scores_gemma":[0.9958184,0.001900002,0.0001345238,0.0012729,0.0008171672,0.00005695459],"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":[0.0001357506,0.00007278902,0.001479978,0.000666859,0.0000455911,0.0002395408,0.001033062,0.00469712,0.03248877,0.004914253,0.01347721,0.9407491],"study_design_scores_gemma":[0.0001590516,0.0003603438,0.03146865,0.001315043,0.0003507763,0.005164516,0.005599553,0.2325815,0.265687,0.03127006,0.4257426,0.0003008415],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0511893,0.004996793,0.8832204,0.0005447772,0.0002199311,0.0003572205,0.001270576,0.01615196,0.04204902],"genre_scores_gemma":[0.1097013,0.00361133,0.8678521,0.0001205329,0.00008020893,0.00009299137,0.002351086,0.002385448,0.01380511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01699017,"threshold_uncertainty_score":0.05683786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01081266313778245,"score_gpt":0.2196844824149203,"score_spread":0.2088718192771379,"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."}}