{"id":"W2020127916","doi":"10.1139/x04-139","title":"An approach to optimizing field data collection in an inventory by compartments","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantile; Forest inventory; Sample (material); Statistics; Mathematics; Volume (thermodynamics); Sampling (signal processing); Observational error; Tree (set theory); Sample size determination; Forest management; Computer science; Forestry; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01070654,0.001876616,0.002403214,0.001273564,0.001043487,0.00215114,0.003355347,0.001426288,0.002049068],"category_scores_gemma":[0.01766804,0.002731757,0.002227134,0.002188074,0.001157777,0.00261383,0.001865638,0.001964117,0.0003591789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004733181,"about_ca_system_score_gemma":0.00550537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03017334,"about_ca_topic_score_gemma":0.0237766,"domain_scores_codex":[0.9954306,0.002831845,0.0002001313,0.0008234322,0.0004860111,0.0002279586],"domain_scores_gemma":[0.9916009,0.005704032,0.0008182017,0.0009243423,0.0008209819,0.0001315926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006255206,0.00005219989,0.001363488,0.00005357432,0.00009578056,0.00002304491,0.0001055365,0.9684882,0.0005820768,0.007464814,0.0002674523,0.02144115],"study_design_scores_gemma":[0.0000192661,0.00008000326,0.0004893639,0.00001888993,0.00003249835,0.0000160813,0.00002854457,0.9900864,0.0007123143,0.007484276,0.001008281,0.00002412639],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00946171,0.00006149833,0.9893005,0.00009389332,0.000007779468,0.0001355984,0.0001198258,0.000232534,0.0005865899],"genre_scores_gemma":[0.147071,0.000125641,0.8493957,0.0001171673,0.00002693473,0.0009478065,0.0004112475,0.0002457742,0.001658747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03017334,"threshold_uncertainty_score":0.05999541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08934704239221573,"score_gpt":0.3432730870213201,"score_spread":0.2539260446291044,"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."}}