{"id":"W2171740519","doi":"10.1007/s10453-006-9024-0","title":"Airborne-pollen map for Olea europaea L. in eastern Andalusia (Spain) using GIS: Estimation models","year":2006,"lang":"en","type":"article","venue":"Aerobiologia","topic":"Allergic Rhinitis and Sensitization","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Pollen; Sampling (signal processing); Kriging; Aerobiology; Geostatistics; Olea; Geography; Vegetation (pathology); Physical geography; Scale (ratio); Interpolation (computer graphics); Cartography; Environmental science; Remote sensing; Spatial variability; Ecology; Mathematics; Computer science; Biology; Statistics; Botany","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000205736,0.0001419963,0.00024988,0.00009254485,0.00006195746,0.00001893826,0.00004817804,0.0001308818,0.0000165068],"category_scores_gemma":[0.00004417097,0.0001212242,0.00005789415,0.0001157252,0.00004569173,0.0001185667,0.00002327021,0.00008514995,0.00001624542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007742993,"about_ca_system_score_gemma":0.00004941447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002005359,"about_ca_topic_score_gemma":0.00005110531,"domain_scores_codex":[0.9990591,0.00005184349,0.0003181096,0.0002550321,0.00007884516,0.0002370857],"domain_scores_gemma":[0.9995643,0.00004787033,0.0001004456,0.000156543,0.00009699177,0.0000338045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003268258,0.001500438,0.07114497,0.0008979443,0.0002635228,0.0005009421,0.002022352,0.7145835,0.08630674,0.04266168,0.01243288,0.06441683],"study_design_scores_gemma":[0.002395334,0.0002281672,0.01267556,0.00018344,0.00003668727,0.00005198522,0.00008765987,0.9807076,0.0004568365,0.002577696,0.0003944561,0.0002045683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8468425,0.0002317285,0.1499915,0.0004437855,0.0001676019,0.0006739961,0.00001552889,0.00008472577,0.001548622],"genre_scores_gemma":[0.9840235,0.00001358892,0.01462232,0.0003216996,0.0001401358,0.000013037,0.0003507703,0.00002277311,0.0004921644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2661242,"threshold_uncertainty_score":0.4943378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03554657464005308,"score_gpt":0.2753046094784352,"score_spread":0.2397580348383822,"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."}}