{"id":"W2134081259","doi":"10.1109/igarss.1993.322063","title":"An assessment of some small window-based spatial features for land-cover classification","year":2002,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Multispectral image; Window (computing); Land cover; Cover (algebra); Computer science; Artificial intelligence; Pattern recognition (psychology); Spatial analysis; Pixel; Contextual image classification; Cartography; Geography; Remote sensing; Image (mathematics); Land use; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001089629,0.0001247573,0.0001462567,0.00009592384,0.0000334772,0.00004095698,0.0001083123,0.0000991175,0.00006401702],"category_scores_gemma":[0.00002561827,0.000118362,0.00005869057,0.00007735632,0.0000268781,0.0001463439,0.000003648231,0.00008472937,0.00001273692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008609694,"about_ca_system_score_gemma":0.00001486787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002788676,"about_ca_topic_score_gemma":0.00003081303,"domain_scores_codex":[0.9992821,0.00002408664,0.0002219305,0.000177795,0.0001372497,0.0001568297],"domain_scores_gemma":[0.9993689,0.00007559407,0.00006014811,0.0003595728,0.00008482934,0.00005092812],"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":[0.00002033038,0.0002198628,0.003533537,0.000202185,0.00004610112,9.577412e-7,0.00008622828,0.1057265,0.8370745,0.001426351,0.003204522,0.04845896],"study_design_scores_gemma":[0.0004782103,0.00005915353,0.1169118,0.00001185066,0.00001763284,7.158647e-7,0.000006667136,0.8379399,0.04368623,0.00005618818,0.0007078986,0.0001238406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.199971,0.00005342117,0.7911418,0.000404324,0.0003605826,0.0006095581,0.00002754327,0.0004404028,0.006991355],"genre_scores_gemma":[0.9559839,0.00000758507,0.04342818,0.00005580939,0.0001300316,0.00001322258,0.00008338073,0.00003534933,0.0002624796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7933882,"threshold_uncertainty_score":0.4826662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03740140460140846,"score_gpt":0.2846642440813523,"score_spread":0.2472628394799439,"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."}}