{"id":"W2162265413","doi":"10.1109/igarss.2000.861593","title":"Integration of remote sensing and morphometric data for geomorphologic mapping","year":2002,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Elevation (ballistics); Linear discriminant analysis; Remote sensing; Normalized Difference Vegetation Index; Geology; Digital elevation model; Vegetation (pathology); Cartography; Geologic map; Computer science; Geography; Geomorphology; Artificial intelligence; Mathematics; Geometry","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":[0.001272215,0.0004193324,0.0002594769,0.002283287,0.0001088149,0.0005104415,0.0003215368,0.0001571654,0.001344034],"category_scores_gemma":[0.003533829,0.0002282641,0.0003108688,0.001490451,0.0002354728,0.0008663416,0.0004991497,0.0002702887,0.0004075115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002763344,"about_ca_system_score_gemma":0.0003728123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002034559,"about_ca_topic_score_gemma":0.005170647,"domain_scores_codex":[0.9993612,0.0002013151,0.00002915254,0.00006130776,0.0003245043,0.00002246139],"domain_scores_gemma":[0.9991406,0.0003584742,0.0001026032,0.0001209304,0.0002495366,0.00002785048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001352011,0.0002085981,0.04624179,0.000266562,0.000156388,0.000128266,0.0001085701,0.07830776,0.02732949,0.007209726,0.001593629,0.8383141],"study_design_scores_gemma":[0.00004298951,0.0004099256,0.1202432,0.00009154308,0.0001755174,0.0005628276,0.0001760811,0.8291893,0.02110164,0.00999906,0.01790363,0.0001042721],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2526694,0.001147379,0.7332269,0.000359399,0.0001180534,0.0001737173,0.0007411662,0.001905711,0.009658254],"genre_scores_gemma":[0.5889509,0.0006942671,0.4065183,0.00004770893,0.00006178206,0.00009177144,0.0009108171,0.00009575117,0.00262867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002283287,"threshold_uncertainty_score":0.006728232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07232818849877129,"score_gpt":0.2452027163210828,"score_spread":0.1728745278223115,"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."}}