{"id":"W344868967","doi":"","title":"Agriculture Canada Central Saskatchewan Vector Soils Data","year":2000,"lang":"en","type":"article","venue":"NASA Technical Reports Server (NASA)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Soil water; Data set; Soil survey; Polygon (computer graphics); Agriculture; Soil map; Geology; Soil science; Database; Geography; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005125492,0.000823735,0.0005803674,0.00435847,0.001756455,0.002675454,0.001104812,0.0003470411,0.1051011],"category_scores_gemma":[0.002301739,0.0005901823,0.0003154974,0.01378583,0.0004444605,0.000922138,0.0008061884,0.0008044867,0.03549882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01470709,"about_ca_system_score_gemma":0.03817222,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9550385,"about_ca_topic_score_gemma":0.9777073,"domain_scores_codex":[0.9990103,0.00005745527,0.00005625539,0.0001884082,0.0005069247,0.0001806202],"domain_scores_gemma":[0.9957299,0.000167007,0.0001305877,0.0002937898,0.003438686,0.0002400717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002891319,0.0001081469,0.03243713,0.0003359797,0.00007592152,0.0002291348,0.0003217929,0.002802025,0.001405293,0.00587373,0.8455931,0.1105286],"study_design_scores_gemma":[0.00007289348,0.0000243511,0.06999201,0.0001809284,0.00003435022,0.00007427824,0.0013602,0.002729177,0.001905896,0.001647225,0.9218864,0.00009228785],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01619839,0.0002631161,0.001812989,0.0006017621,0.0001156096,0.0003034357,0.8827791,0.001292277,0.09663329],"genre_scores_gemma":[0.05854126,0.001151793,0.009239454,0.0004661464,0.00002118961,0.000596251,0.7361494,0.0006209965,0.1932135],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1051011,"threshold_uncertainty_score":0.3515983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01370595508256043,"score_gpt":0.2300219447644981,"score_spread":0.2163159896819376,"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."}}