{"id":"W2088715406","doi":"10.1109/rose.2013.6698421","title":"A supervised training and learning method for building identification in remotely sensed imaging","year":2013,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Identification (biology); Artificial intelligence; Segmentation; Computer vision; Object (grammar); Region of interest; Support vector machine; Training (meteorology); Precision and recall; Image segmentation; Pattern recognition (psychology); Machine learning; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006100787,0.0001375551,0.0001711487,0.0002214137,0.00008450107,0.0001931228,0.00006090913,0.00005532414,0.00001128206],"category_scores_gemma":[0.0004031391,0.0001516338,0.00003341105,0.0001997319,0.00001994612,0.0004081868,0.00001303976,0.0001937844,0.00001033396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007575734,"about_ca_system_score_gemma":0.000008962139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001069346,"about_ca_topic_score_gemma":0.00001037226,"domain_scores_codex":[0.9989741,0.00007871912,0.0003273273,0.0002622973,0.00008964165,0.0002679347],"domain_scores_gemma":[0.9993278,0.0003459018,0.0000413154,0.0001552165,0.00007514783,0.00005464492],"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":[0.000001458595,0.000002421826,0.0001555944,0.00004153005,0.000004675985,6.604137e-7,0.001171247,0.005590882,0.7014457,0.00007413026,0.00006310577,0.2914486],"study_design_scores_gemma":[0.0003507274,0.000004698483,0.01238973,0.0000401925,0.000007675779,0.00001591615,0.001259195,0.9664009,0.01848537,0.0005115368,0.0003645084,0.0001695164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3388866,0.00005399304,0.6593623,0.0002822462,0.00008155899,0.0003424631,3.412472e-7,0.0003046199,0.0006858471],"genre_scores_gemma":[0.6550533,0.00000732379,0.3446918,0.00002579382,0.00002990124,0.00001197658,0.000007560927,0.00003691265,0.00013542],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9608101,"threshold_uncertainty_score":0.6183448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02455961322029594,"score_gpt":0.271228264243122,"score_spread":0.2466686510228261,"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."}}