{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001006225,0.00073765,0.0007512401,0.001368579,0.0005943029,0.0008035612,0.001067253,0.0005440652,0.0008922911],"category_scores_gemma":[0.004384093,0.0004146784,0.0007866368,0.002135817,0.000390079,0.00088833,0.0012115,0.0005748837,0.00030686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001302269,"about_ca_system_score_gemma":0.0011777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01992914,"about_ca_topic_score_gemma":0.02678385,"domain_scores_codex":[0.9990195,0.0002401006,0.00006806265,0.0002225966,0.0003846829,0.00006504475],"domain_scores_gemma":[0.9981686,0.0006165692,0.0002718061,0.000305749,0.0005735134,0.0000637934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002000452,0.00009934569,0.005666832,0.0001840633,0.0001354803,0.00010191,0.0002250533,0.8314573,0.01657154,0.002847957,0.001365368,0.141145],"study_design_scores_gemma":[0.000003907774,0.00001631462,0.001556611,0.000005666698,0.000004504072,0.00001421742,0.00003969338,0.9941208,0.002468129,0.001261691,0.0004982587,0.00001022941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05811612,0.0001157824,0.9396042,0.00009385356,0.00002152186,0.0001185111,0.0004654214,0.0008339559,0.0006306726],"genre_scores_gemma":[0.5027888,0.00007074057,0.4952336,0.00003202958,0.000009103107,0.0001810645,0.001033639,0.00007795442,0.0005731307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01992914,"threshold_uncertainty_score":0.0396263,"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."}}