{"id":"W2090883311","doi":"10.1016/j.agee.2012.02.007","title":"Spatially locating soil classes within complex soil polygons – Mapping soil capability for agriculture in Saskatchewan Canada","year":2012,"lang":"en","type":"article","venue":"Agriculture Ecosystems & Environment","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; Natural Resources Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Normalized Difference Vegetation Index; Soil map; Digital soil mapping; Environmental science; Remote sensing; Ancillary data; Land cover; Vegetation (pathology); Soil series; Soil science; Soil classification; Land use; Computer science; Cartography; Soil water; Geography; Climate change; Geology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008328605,0.0005813223,0.0005995302,0.00003721539,0.0004986412,0.00007612978,0.0004245256,0.0002439877,0.0002813714],"category_scores_gemma":[0.00008099434,0.0004368469,0.0001365529,0.0002785639,0.0001010407,0.0002821993,0.0002929835,0.0003705726,0.00008464176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002127299,"about_ca_system_score_gemma":0.0001066028,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5988572,"about_ca_topic_score_gemma":0.969283,"domain_scores_codex":[0.9960908,0.0001943132,0.0009672622,0.0008098046,0.0007554672,0.00118231],"domain_scores_gemma":[0.9983163,0.0002205843,0.0005150176,0.0004814144,0.00001780874,0.0004488325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003802753,0.001070129,0.3360304,0.0006024196,0.0002140011,0.00003036289,0.01486207,0.1064293,0.4379267,0.0002795884,0.09902895,0.003488092],"study_design_scores_gemma":[0.00197513,0.0001637483,0.7269291,0.00033139,0.0001512402,0.0001032763,0.03905,0.01008904,0.02217759,0.0001563547,0.1963954,0.00247777],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924803,0.000263394,0.002566364,0.0005908104,0.0007556676,0.001390997,0.0002785248,0.00006131026,0.001612624],"genre_scores_gemma":[0.9951242,0.00001665908,0.002328671,0.0003497069,0.0004528928,0.0003850555,0.0003664064,0.00003833103,0.0009380305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4157491,"threshold_uncertainty_score":0.9998083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01085611407283964,"score_gpt":0.1888729684382379,"score_spread":0.1780168543653982,"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."}}