{"id":"W2810267240","doi":"10.1002/env.2608","title":"Bayesian spatial analysis of hardwood tree counts in forests via MCMC","year":2019,"lang":"en","type":"preprint","venue":"Environmetrics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research; St. Michael's Hospital; University of Toronto","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Bayesian probability; Bayesian inference; Statistics; Sampling (signal processing); Computer science; Spatial analysis; Stratified sampling; Sample (material); Spatial distribution; Forestry; Data mining; Mathematics; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003709857,0.0003583822,0.0007815239,0.001407354,0.001019271,0.001106024,0.002083986,0.0008096514,0.002534754],"category_scores_gemma":[0.01989787,0.0007414655,0.0007733333,0.001387663,0.00119093,0.001172884,0.001158481,0.00130643,0.000293138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002614618,"about_ca_system_score_gemma":0.002596564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1588124,"about_ca_topic_score_gemma":0.1752609,"domain_scores_codex":[0.9990118,0.0005028913,0.00003876432,0.0001865762,0.0001699191,0.00009000013],"domain_scores_gemma":[0.991757,0.006225121,0.000462087,0.0006296032,0.0007627563,0.0001634862],"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":[0.00006644343,0.00002946476,0.008275808,0.00002884791,0.00006012897,0.00003979493,0.000110413,0.9491552,0.0004223513,0.01479619,0.0006055274,0.02640992],"study_design_scores_gemma":[0.000005770526,0.000001995332,0.0007016257,0.000005564819,0.000003032163,0.000003232783,0.000007308862,0.9943994,0.00009802006,0.004635738,0.0001346164,0.000003703894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1726438,0.0003234789,0.8243581,0.0003130013,0.00002538343,0.00006077901,0.0004367869,0.0007273924,0.001111183],"genre_scores_gemma":[0.778098,0.0001510344,0.2184073,0.0001209398,0.00004237151,0.0001340477,0.001100248,0.0002091014,0.001736906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1588124,"threshold_uncertainty_score":0.3157761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126822790083642,"score_gpt":0.2284014587596479,"score_spread":0.2171332308588115,"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."}}