{"id":"W3011757782","doi":"10.1109/access.2020.2980134","title":"An Integrated Multiscale Geometric Analysis Approach for Automatic Extraction of Power Lines From High Resolution Remote Sensing Images","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Computer science; Artificial intelligence; Thresholding; Computer vision; Brightness; Ground truth; Noise (video); Image resolution; Power (physics); Orientation (vector space); Pattern recognition (psychology); Image (mathematics); Mathematics; Physics; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001249365,0.0001708156,0.0003323481,0.0004636123,0.00006744154,0.0001037828,0.0001745241,0.0001528705,0.00002227648],"category_scores_gemma":[0.00008695782,0.0001613475,0.0001196927,0.00190213,0.00002013454,0.0007186966,0.00001101243,0.0001570103,0.000002432927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006101986,"about_ca_system_score_gemma":0.000009623211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008554716,"about_ca_topic_score_gemma":0.00001293308,"domain_scores_codex":[0.9989594,0.00004068209,0.0003745767,0.0002756273,0.0001705939,0.0001791413],"domain_scores_gemma":[0.9993361,0.0001070068,0.0001554777,0.0002086454,0.0001297472,0.00006307288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000336638,0.00004053253,0.0002035309,0.0001151856,0.0003582551,0.000002244322,0.0001360169,0.7012758,0.2181725,4.547265e-7,0.0004800712,0.07918168],"study_design_scores_gemma":[0.0002407288,0.00003026764,0.01400542,0.00002048657,0.0003490508,0.000001286412,0.00006349385,0.8767882,0.1082704,0.00001546832,0.00005684955,0.0001583254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4533114,0.00007706852,0.5457761,0.00001742855,0.0001871332,0.0001238133,0.00005015534,0.0004361406,0.0000207617],"genre_scores_gemma":[0.9055429,0.00002046144,0.09398406,0.00001518873,0.0001222857,0.000002040364,0.0002743258,0.00003364108,0.00000515897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4522314,"threshold_uncertainty_score":0.6579559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02493703409774106,"score_gpt":0.2910357755823995,"score_spread":0.2660987414846585,"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."}}