{"id":"W2163432472","doi":"10.1139/x99-198","title":"Predicting tree mortality from diameter growth: a comparison of maximum likelihood and Bayesian approaches","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Statistics; Bayesian probability; Mortality rate; Mathematics; Biology; Parametric model; Econometrics; Parametric statistics; Demography","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":[0.02739503,0.001349544,0.001477388,0.003619298,0.0009402591,0.002038194,0.002578379,0.00204956,0.001251272],"category_scores_gemma":[0.0941081,0.001190574,0.001469572,0.001924481,0.001057701,0.003635541,0.002079536,0.001868341,0.0004998099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001399807,"about_ca_system_score_gemma":0.001851901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01129313,"about_ca_topic_score_gemma":0.01208289,"domain_scores_codex":[0.9919813,0.006133577,0.0003882037,0.0004133062,0.0009296578,0.0001538283],"domain_scores_gemma":[0.9290325,0.06541751,0.001559513,0.001309279,0.002194815,0.0004863851],"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":[0.001086846,0.0003331034,0.04000129,0.000494788,0.0006821494,0.0001259366,0.0006636796,0.5965365,0.0009474586,0.02331859,0.001944564,0.3338651],"study_design_scores_gemma":[0.0001209182,0.00008650423,0.007271294,0.0001306519,0.00009473588,0.0001067117,0.00009798579,0.9545726,0.0003886648,0.03619012,0.0008487637,0.00009117892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06468777,0.002437339,0.9291862,0.000817748,0.00003457826,0.0001247001,0.0002050472,0.0006517635,0.001854982],"genre_scores_gemma":[0.5202928,0.002299085,0.4739957,0.0003161229,0.0001588509,0.0005066929,0.0009642105,0.000336648,0.001129867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02739503,"threshold_uncertainty_score":0.1448805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05999517510377134,"score_gpt":0.2889556732437321,"score_spread":0.2289604981399608,"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."}}