{"id":"W2020437530","doi":"10.1155/2012/430347","title":"Digital Mapping of Soil Drainage Classes Using Multitemporal RADARSAT-1 and ASTER Images and Soil Survey Data","year":2012,"lang":"en","type":"article","venue":"Applied and Environmental Soil Science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Canadian Space Agency","keywords":"Soil survey; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Watershed; Algorithm; Drainage; Artificial intelligence; Geology; Computer science; Soil water; Remote sensing; Digital elevation model; Soil science; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008864216,0.0002177125,0.0002166351,0.00005472485,0.0003842971,0.0001625329,0.0002990647,0.00005183793,0.00003994137],"category_scores_gemma":[0.00004265547,0.0002001519,0.00001280125,0.0001832879,0.002592923,0.001233196,0.002011859,0.0001029731,0.00001054091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005916185,"about_ca_system_score_gemma":0.00001147774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00117058,"about_ca_topic_score_gemma":0.00006957871,"domain_scores_codex":[0.9981769,0.0000235704,0.0002557037,0.0006019729,0.0004286065,0.0005131901],"domain_scores_gemma":[0.9991165,0.0001145451,0.0001359836,0.0003517097,0.000002064691,0.0002792366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001441552,0.00009519275,0.8316528,0.00002623395,0.000009581804,0.000002142675,0.0008607586,0.0001009944,0.1114121,0.00005105204,0.00006266506,0.05571204],"study_design_scores_gemma":[0.0004335446,0.00002576204,0.9854595,0.00001674489,0.00001577792,0.00002809932,0.001558801,0.008499715,0.002688467,0.0001660962,0.0007568207,0.0003506542],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994164,0.0002187078,0.0009878943,0.0000176624,0.00006139552,0.0001447674,0.0002385738,0.0000122569,0.004154724],"genre_scores_gemma":[0.9972006,0.0001158772,0.002442742,0.0000692499,0.00002719239,0.000001844639,0.00005798454,0.00001499003,0.00006952443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1538067,"threshold_uncertainty_score":0.9553735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0321856655602157,"score_gpt":0.2377145342222694,"score_spread":0.2055288686620537,"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."}}