{"id":"W2072007972","doi":"10.1139/x11-109","title":"Improving tree survival prediction with forecast combination and disaggregation","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tree (set theory); Computer science; Estimator; Predictive modelling; Statistics; Econometrics; Mathematics; Machine learning","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":[0.001807868,0.000718091,0.00111118,0.0009627361,0.0002427292,0.000633318,0.0004299847,0.0004399396,0.0006688291],"category_scores_gemma":[0.003788714,0.0004392398,0.0008014815,0.001042963,0.0001444982,0.00103393,0.0006953342,0.0008390784,0.0002430889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004924522,"about_ca_system_score_gemma":0.0006866097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01593593,"about_ca_topic_score_gemma":0.01649972,"domain_scores_codex":[0.9993062,0.0002177921,0.00005306738,0.0001899537,0.0001582071,0.00007485498],"domain_scores_gemma":[0.9981925,0.000817751,0.0001852553,0.0003709916,0.0003558131,0.00007765526],"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":[0.0002390689,0.0001051197,0.04218008,0.00005818828,0.0002697466,0.0001150878,0.0001387172,0.783804,0.01196696,0.0005688926,0.00142176,0.1591325],"study_design_scores_gemma":[0.000009381375,0.00002738466,0.007249472,0.000002201927,0.0000315771,0.00001398526,0.00001192089,0.9903036,0.00161366,0.0003727065,0.0003456469,0.00001846347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5946866,0.0003138499,0.3986228,0.0002645577,0.00008423681,0.00005950314,0.001139936,0.002900607,0.001927864],"genre_scores_gemma":[0.9300858,0.00006506638,0.06835155,0.00003923771,0.00002990045,0.00002968369,0.0009772928,0.00006399259,0.0003574312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01593593,"threshold_uncertainty_score":0.03168637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03474819151222724,"score_gpt":0.241705284088242,"score_spread":0.2069570925760147,"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."}}