{"id":"W3035465462","doi":"","title":"Non-Parametric Approach for Trend Delineation in the Canadian Prairie","year":2006,"lang":"en","type":"article","venue":"AGUFM","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Environmental science; Computer science; Geography","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":[0.0001951144,0.00004383533,0.00003920355,0.00004746604,0.00009594037,0.00002870243,0.00009437475,0.00002683575,0.00002196046],"category_scores_gemma":[0.00003148606,0.00003328683,0.00001405457,0.0003054595,0.00002625894,0.00003466637,0.000008546034,0.00003682058,0.00001962252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009512059,"about_ca_system_score_gemma":0.00001270887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5113151,"about_ca_topic_score_gemma":0.7965288,"domain_scores_codex":[0.9995475,0.000008584057,0.00008301937,0.0001086627,0.00009272822,0.0001595109],"domain_scores_gemma":[0.9998176,0.00004549663,0.00002094688,0.00008711272,0.000002562514,0.00002624384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001597876,0.0002950554,0.3669692,0.00002920728,0.00001029895,0.00001759882,0.001416859,0.2959861,0.0002106062,0.01233271,0.1638414,0.1588749],"study_design_scores_gemma":[0.0003186316,0.00003130957,0.5248431,0.000001923617,0.000008064414,0.000003085565,0.0001318797,0.3741035,0.00002843433,0.002057089,0.09834819,0.0001248198],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6457711,0.00007248549,0.09247842,0.002067591,0.0001689149,0.001328556,0.00007134558,0.00002364426,0.2580179],"genre_scores_gemma":[0.991894,5.470878e-7,0.007185525,0.0002644054,0.00003558667,0.0000507775,0.00008896509,0.000003801589,0.0004763746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3461229,"threshold_uncertainty_score":0.4919391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602967717686555,"score_gpt":0.2313979155512796,"score_spread":0.2153682383744141,"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."}}