{"id":"W1684243805","doi":"10.1139/cjfr-2013-0490","title":"A sampling design for a large area forest inventory: case Tanzania","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forest inventory; Sampling design; Stratified sampling; Sampling (signal processing); Tanzania; Systematic sampling; Environmental science; Environmental resource management; Forestry; Geography; Statistics; Forest management; Computer science; Mathematics; Population; Environmental planning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00340553,0.00009623283,0.0001495777,0.0002505423,0.000707101,0.0001288448,0.000333975,0.00007737379,0.0001663735],"category_scores_gemma":[0.0008993064,0.00008562301,0.00008849498,0.0003754828,0.0002068512,0.0001190353,0.00002908753,0.0003564273,0.00007496978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004160964,"about_ca_system_score_gemma":0.0004136991,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01337712,"about_ca_topic_score_gemma":0.3702975,"domain_scores_codex":[0.998364,0.0001681519,0.0002593264,0.0001785366,0.0003327299,0.0006972612],"domain_scores_gemma":[0.9981635,0.0003562931,0.00009424186,0.000273664,0.0001356523,0.0009766346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002249716,0.0002196373,0.6190054,0.00008789007,0.0001319488,0.001936586,0.005028745,0.05029441,0.002454105,0.02122365,0.2547627,0.04462993],"study_design_scores_gemma":[0.001867224,0.001138739,0.05705004,0.0001664253,0.00004327412,0.005670125,0.0009705348,0.07573842,0.000301802,0.0426944,0.8138885,0.0004705543],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8564454,0.00005904026,0.1390348,0.000938889,0.00009646385,0.0004059999,0.00001395595,0.000005839188,0.002999566],"genre_scores_gemma":[0.9904438,0.000003078782,0.008895953,0.00007627458,0.0001672428,0.000005855112,0.000003037491,0.00002225582,0.0003825016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5619554,"threshold_uncertainty_score":0.9931929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1025917883704716,"score_gpt":0.3308812670855545,"score_spread":0.2282894787150829,"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."}}