{"id":"W2991877752","doi":"10.1111/ejss.12923","title":"Forecasting potato tuber yield using a soil electromagnetic induction method","year":2019,"lang":"en","type":"article","venue":"European Journal of Soil Science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Prince Edward Island","funders":"","keywords":"Yield (engineering); Soil water; Soil science; Precision agriculture; Potash; Water content; Agronomy; Mathematics; Environmental science; Fertilizer; Geology; Agriculture; Ecology; Materials science; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003554804,0.000121484,0.0001506949,0.0001260592,0.0002309194,0.0001247268,0.0004841858,0.0000140278,0.0004189067],"category_scores_gemma":[0.0003967371,0.0001032484,0.00005848342,0.0007124,0.0002230754,0.0005693257,0.0002223817,0.0002682099,0.0001562424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001535861,"about_ca_system_score_gemma":0.00007303208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001168349,"about_ca_topic_score_gemma":0.00001006673,"domain_scores_codex":[0.9981312,0.0001447999,0.0003881103,0.0002625431,0.0006685691,0.0004048364],"domain_scores_gemma":[0.9991201,0.00008465422,0.0003727241,0.0001745419,0.00006255085,0.0001854472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003799067,0.00005729665,0.01199742,0.00001366151,0.00001182818,0.0001771962,0.001521907,0.06672534,0.7965519,0.00009444373,0.0004144075,0.1223966],"study_design_scores_gemma":[0.002012063,0.003665003,0.3175777,0.0006913204,0.0001470349,0.007741338,0.002005327,0.606862,0.05097054,0.002233354,0.004677617,0.001416613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9587398,0.0000269086,0.01174718,0.00008062549,0.0004948544,0.00005843909,4.908497e-7,0.000008681536,0.02884303],"genre_scores_gemma":[0.9522393,0.00000704303,0.04723735,0.0001495698,0.0001133894,1.501696e-7,9.996393e-8,0.00001559845,0.0002375194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7455813,"threshold_uncertainty_score":0.4586734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03342315746769082,"score_gpt":0.2513587538617493,"score_spread":0.2179355963940585,"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."}}