{"id":"W2186548078","doi":"10.1139/cjfr-2015-0237","title":"Mechanistic and statistical approaches to predicting wind damage to individual maritime pine (<i>Pinus pinaster</i>) trees in forests","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Tree Root and Stability Studies","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut National de la Recherche Agronomique; Agence Nationale de la Recherche; Natural Environment Research Council; Sight Research UK","keywords":"Pinus pinaster; Environmental science; Storm; Pinus <genus>; Climate change; Forestry; Ecology; Meteorology; Geography; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.001770857,0.0001453982,0.0002936938,0.0009205519,0.0001108237,0.0001622929,0.0002909677,0.00007062164,0.00001426826],"category_scores_gemma":[0.001388513,0.0001385198,0.00002339304,0.0004925533,0.0001227791,0.0001432545,0.00008287147,0.0005297234,0.0000116078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003413903,"about_ca_system_score_gemma":0.0004949991,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003693137,"about_ca_topic_score_gemma":0.8956891,"domain_scores_codex":[0.9981338,0.0001103439,0.0003640971,0.0001707107,0.000494407,0.000726687],"domain_scores_gemma":[0.9977017,0.0002562777,0.00001946927,0.000150535,0.0001689804,0.001703009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006617527,0.00001846237,0.9733007,0.00007454797,0.00005054297,0.0005864875,0.004504767,0.01025105,0.00001171709,0.001510467,0.003500283,0.006124794],"study_design_scores_gemma":[0.0005326967,0.0006660066,0.9915726,0.0001321964,0.000009737359,0.00006216394,0.00136945,0.002092394,0.00001372012,0.002407873,0.0009995645,0.0001416593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99563,0.0004303919,0.001891551,0.0007929825,0.0001234533,0.0002913463,0.0001149773,0.000009602753,0.0007157222],"genre_scores_gemma":[0.9983659,0.000002017997,0.001380526,0.00001077206,0.0001554156,0.000009972157,0.000005150724,0.00002562849,0.00004460959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.891996,"threshold_uncertainty_score":0.5648672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1630341758865833,"score_gpt":0.2995825195492083,"score_spread":0.136548343662625,"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."}}