{"id":"W7162071187","doi":"10.82308/29566","title":"Optimization of sampling designs for validating digital soil maps","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital soil mapping; Sampling (signal processing); Latin hypercube sampling; Kriging; Sampling design; Soil map; Stratified sampling; Simple random sample; Precision agriculture; Sample (material)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001080926,0.0001304488,0.0001539422,0.00003362725,0.00008480847,0.00004623412,0.0001154709,0.0000968188,0.0006027829],"category_scores_gemma":[0.0002030833,0.0001089303,0.0000600779,0.00006171731,0.00002043386,0.0001391048,0.000027037,0.00003560525,0.00002732319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004741209,"about_ca_system_score_gemma":0.00001701834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005633429,"about_ca_topic_score_gemma":0.00004926696,"domain_scores_codex":[0.9991547,0.000005710923,0.0002720585,0.0002298118,0.0001719299,0.0001657538],"domain_scores_gemma":[0.9994079,0.0001794342,0.0002319759,0.0001156079,0.00002681655,0.00003826766],"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":[0.0002644329,0.0002356945,0.005857213,0.001074805,0.000197277,0.000002200887,0.002935206,0.3142225,0.05185161,0.005703059,0.01017685,0.6074792],"study_design_scores_gemma":[0.01097875,0.002147475,0.01703299,0.006297578,0.00138492,0.00002052957,0.02141498,0.4074675,0.3293773,0.1579051,0.0348011,0.01117185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009853651,0.000009113935,0.9078292,0.000009693553,0.000270891,0.0003337319,0.0001992483,0.00003093261,0.08146358],"genre_scores_gemma":[0.5007478,0.0000555195,0.4284838,0.00006794966,0.0002230176,0.0001844617,0.009144331,0.000160035,0.06093312],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5963073,"threshold_uncertainty_score":0.6600051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0352987103239939,"score_gpt":0.2775347353033806,"score_spread":0.2422360249793867,"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."}}