{"id":"W2276037235","doi":"10.5558/tfc2012-008","title":"Sampling design and precision of basal area growth and stand structure in uneven-aged northern hardwoods","year":2012,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Forest ecology and management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts (Québec)","funders":"","keywords":"Sampling (signal processing); Basal area; Statistics; Sampling design; Systematic sampling; Mathematics; Sample size determination; Range (aeronautics); Selection (genetic algorithm); Sample (material); Sampling bias; Environmental science; Ecology; Biology; Computer science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01924115,0.0004163248,0.0004739578,0.0006609191,0.0008056842,0.0006074032,0.0009527467,0.0004808971,0.00039006],"category_scores_gemma":[0.04511338,0.0003570297,0.0003466182,0.0007161204,0.0013404,0.0004227476,0.0006054643,0.0002430069,0.000157993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001983105,"about_ca_system_score_gemma":0.001877612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07317942,"about_ca_topic_score_gemma":0.2069342,"domain_scores_codex":[0.9771246,0.01478184,0.000940635,0.002842724,0.003825245,0.0004849692],"domain_scores_gemma":[0.9536191,0.02671391,0.008387246,0.005016509,0.005620711,0.0006425152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001363142,0.0002410842,0.8802542,0.0002700967,0.0003224552,0.0001341075,0.00269629,0.009999344,0.01851538,0.0003755965,0.0003432333,0.08548507],"study_design_scores_gemma":[0.00008431853,0.001171087,0.98382,0.00004909367,0.00008101243,0.0001127939,0.0004196498,0.006410296,0.006612794,0.0001928159,0.001013657,0.0000325492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981044,0.0003526354,0.01741475,0.00003022766,0.00001177742,0.0002530907,0.0001477611,0.00004295786,0.0007027491],"genre_scores_gemma":[0.9774712,0.0001052551,0.02142467,0.00004523926,0.000008425181,0.0003264476,0.000279206,0.000009474248,0.0003300875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07317942,"threshold_uncertainty_score":0.1455069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01561575125990844,"score_gpt":0.2298012262259916,"score_spread":0.2141854749660832,"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."}}