{"id":"W2044869925","doi":"10.1890/08-2334.1","title":"Unlocking the forest inventory data: relating individual tree performance to unmeasured environmental factors","year":2010,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Forest ecology and management","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McMaster University","funders":"U.S. Forest Service","keywords":"Forest inventory; Data set; Environmental science; Covariate; Ecology; Tree (set theory); Statistics; Calibration; Bayesian probability; Scale (ratio); Forestry; Mathematics; Geography; Forest management; Biology; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009427064,0.0008244767,0.0005858483,0.001574087,0.0005007529,0.001269602,0.0009829851,0.0006423652,0.0003773128],"category_scores_gemma":[0.04189947,0.0005630235,0.000539833,0.002853479,0.0009862069,0.001758681,0.00118742,0.001152305,0.0002184827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005923256,"about_ca_system_score_gemma":0.0006191768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02241799,"about_ca_topic_score_gemma":0.02267767,"domain_scores_codex":[0.9943396,0.003477708,0.0002778696,0.0009447106,0.0007622534,0.0001978646],"domain_scores_gemma":[0.9677412,0.02244525,0.003965844,0.004103038,0.00144117,0.0003035333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007657523,0.00006200381,0.9591799,0.00004441295,0.000247453,0.0000435555,0.0003467076,0.01449624,0.001277009,0.0007309059,0.0002706499,0.02322458],"study_design_scores_gemma":[0.00001691308,0.0001080273,0.878715,0.00004383339,0.00009892573,0.0001660411,0.0003404479,0.1137549,0.002287763,0.002806078,0.001595192,0.00006685904],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8995124,0.0003417559,0.09466576,0.0001457665,0.00001561483,0.00007892709,0.002413818,0.0001584057,0.002667489],"genre_scores_gemma":[0.9594532,0.000119246,0.03804678,0.00007299322,0.00001739688,0.00008075337,0.001966107,0.00004350322,0.0002000816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02241799,"threshold_uncertainty_score":0.04985571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03112604281591704,"score_gpt":0.2358786023298124,"score_spread":0.2047525595138953,"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."}}