{"id":"W2395968533","doi":"10.1080/10106049.2015.1120351","title":"An evaluation of Radarsat-2 individual and combined image dates for land use/cover mapping","year":2015,"lang":"en","type":"article","venue":"Geocarto International","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; National Aeronautics and Space Administration; U.S. Department of Agriculture; European Space Agency; U.S. Geological Survey; George Mason University","keywords":"Thematic map; Land cover; Geography; Remote sensing; Cartography; Thematic Mapper; Land use; Contextual image classification; Cover (algebra); Physical geography; Satellite imagery; Image (mathematics); Artificial intelligence; Computer science; Engineering","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":[0.0006016516,0.00008359966,0.00009374756,0.0001125118,0.00001638971,0.0001009399,0.00009977266,0.0000441258,0.00001468716],"category_scores_gemma":[0.0003129681,0.00009011355,0.0000183645,0.00005943669,0.00003584471,0.0005383646,0.00001964305,0.00004663775,0.000007086423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006721743,"about_ca_system_score_gemma":0.00003816633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002664418,"about_ca_topic_score_gemma":0.000009213329,"domain_scores_codex":[0.9991633,0.00003222335,0.000187372,0.000133474,0.0003907721,0.00009281516],"domain_scores_gemma":[0.999109,0.00006767252,0.00005582044,0.0001375665,0.0005820363,0.00004794279],"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":[0.0004903649,0.0005059054,0.2032215,0.0003165399,0.001584747,0.000008962986,0.01358967,0.136671,0.3537313,0.004050549,0.05063296,0.2351966],"study_design_scores_gemma":[0.001588104,0.00004407291,0.08817147,0.00002508585,0.00004588868,0.000007069926,0.00009760878,0.8940496,0.00842318,0.001281966,0.006141115,0.0001248721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603275,0.00004511318,0.0378138,0.0001382881,0.0004978252,0.0002573189,0.0001096652,0.00007422498,0.0007362466],"genre_scores_gemma":[0.9795907,0.00000390808,0.01966495,0.00001812838,0.00009852576,0.000004902643,0.000564848,0.0000178458,0.00003615717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7573786,"threshold_uncertainty_score":0.3674724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09271145703678141,"score_gpt":0.3011447024941359,"score_spread":0.2084332454573545,"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."}}