{"id":"W2093080986","doi":"10.5194/isprsarchives-xl-2-187-2014","title":"Spatially Constrained Geospatial Data Clustering for Multilayer Sensor-Based Measurements","year":2014,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Nebraska-Lincoln","keywords":"Cluster analysis; Raster data; Geospatial analysis; Raster graphics; Mean squared error; Computer science; Field (mathematics); Data mining; Process (computing); Soil map; Algorithm; Remote sensing; Mathematics; Geography; Soil science; Statistics; Artificial intelligence; Geology; Soil water","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.002265343,0.0004339763,0.0004271028,0.0004730054,0.00147184,0.0006788761,0.002210673,0.00009214292,0.00001885079],"category_scores_gemma":[0.001638292,0.0002877853,0.0002769585,0.0005914784,0.003034832,0.0004632191,0.00157821,0.0003254936,0.000004813569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000571414,"about_ca_system_score_gemma":0.0001751386,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8009241,"about_ca_topic_score_gemma":0.3475274,"domain_scores_codex":[0.9951804,0.000301931,0.001407403,0.0005394453,0.001987244,0.0005835419],"domain_scores_gemma":[0.9958495,0.00143768,0.001622512,0.0007073806,0.0002058383,0.0001771211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001737909,0.0000214907,0.0006436807,0.00004139956,0.00006663425,1.389791e-7,0.001328576,0.02119753,0.00297057,0.000003568871,0.00007572488,0.9734769],"study_design_scores_gemma":[0.00127041,0.0001554704,0.004541925,0.0002484527,0.00005817721,0.0000448727,0.0007120637,0.9796023,0.003635444,0.002309481,0.007079556,0.0003418934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005160409,0.000007446589,0.9811841,0.003475734,0.001768086,0.0009012297,0.000256429,0.00004363049,0.007202951],"genre_scores_gemma":[0.9772561,0.00002744135,0.02069482,0.001690127,0.000151295,6.886175e-7,0.0001086677,0.0000145333,0.00005637764],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.973135,"threshold_uncertainty_score":0.9999574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03787199268381779,"score_gpt":0.2734684385566926,"score_spread":0.2355964458728748,"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."}}