{"id":"W2126455054","doi":"10.1139/x05-057","title":"Tree diameter distribution modelling: introducing the logitlogistic distribution","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Mathematical Sciences","keywords":"Weibull distribution; Mathematics; Logistic distribution; Kurtosis; Statistics; Logit; Logistic regression; Shape parameter; Generalized beta distribution; Distribution (mathematics); Univariate distribution; Range (aeronautics); Log-logistic distribution; Distribution fitting; Econometrics; Probability distribution; Mathematical analysis; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002475268,0.00008667258,0.0001094194,0.0000755307,0.0005571574,0.00008572736,0.0005200183,0.00007173313,0.001018097],"category_scores_gemma":[0.0005161296,0.00005938448,0.00006809176,0.0003474712,0.0007574527,0.0002257946,0.00007227531,0.0005597507,0.0003416742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009408249,"about_ca_system_score_gemma":0.0001827444,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005895284,"about_ca_topic_score_gemma":0.219696,"domain_scores_codex":[0.9984155,0.0001844859,0.0002711129,0.0001607214,0.000377817,0.0005903591],"domain_scores_gemma":[0.9990652,0.0001630547,0.00008739642,0.0002477031,0.0000756989,0.0003609918],"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":[0.0000600906,0.0000689901,0.1684123,0.000009272941,0.00004463492,0.0001473501,0.0003898737,0.4587977,0.00001884466,0.04154763,0.3084051,0.02209826],"study_design_scores_gemma":[0.0005120963,0.0004435442,0.4868656,0.00003232541,0.000036282,0.0001054992,0.0002540086,0.03726866,0.00009418553,0.03221706,0.4419606,0.0002101673],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7878312,0.0002795491,0.1870348,0.01765254,0.0003905419,0.0003886916,0.00005395398,0.000008376319,0.006360359],"genre_scores_gemma":[0.9986146,0.00002937078,0.0003198014,0.00008517384,0.0002672431,0.000007277462,0.00003166966,0.000006805428,0.0006380693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4215291,"threshold_uncertainty_score":0.9998951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04453098477897103,"score_gpt":0.2811273938634593,"score_spread":0.2365964090844882,"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."}}