{"id":"W45574125","doi":"","title":"Semi-automatic Road Extraction from Very High Resolution Remote Sensing Imagery by RoadModeler","year":2009,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Remote sensing; Extraction (chemistry); Aerial imagery; High resolution; Artificial intelligence; Geography; Computer science; Computer vision; Cartography; Chemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001149292,0.0004019323,0.0005133085,0.0003498693,0.0002193343,0.00004894243,0.0001881576,0.0007234006,0.0001148531],"category_scores_gemma":[0.00001031821,0.000521253,0.0001905117,0.0002255252,0.00003305209,0.0006706162,0.0000164589,0.0005577148,0.00009329317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000381394,"about_ca_system_score_gemma":0.00003839324,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06385864,"about_ca_topic_score_gemma":0.007522685,"domain_scores_codex":[0.9985182,0.00006343444,0.0002570943,0.0004246346,0.000375579,0.0003610037],"domain_scores_gemma":[0.9991049,0.00003547313,0.0002954132,0.0003606825,0.0001072225,0.00009635776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000198257,0.00007467374,0.000006268533,0.0005401905,0.0004049059,0.0001160758,0.01295828,0.02508429,0.4879231,0.000003080752,0.04435652,0.4283343],"study_design_scores_gemma":[0.0009265853,0.00008290248,0.008864578,0.001434966,0.0006349703,0.00001663258,0.0149035,0.9381604,0.03236335,0.0003268261,0.001135733,0.001149534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993356,0.0005162872,0.003203317,0.00009475304,0.001089025,0.0002146825,0.00007442333,0.0009714114,0.0004800556],"genre_scores_gemma":[0.8901401,0.001216609,0.01493278,0.00001601267,0.000276326,1.389816e-7,0.006794877,0.0001625535,0.08646059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9130761,"threshold_uncertainty_score":0.9997239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005586475464252226,"score_gpt":0.1892461809450472,"score_spread":0.183659705480795,"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."}}