{"id":"W2801256412","doi":"10.1002/eap.1727","title":"Response diversity, functional redundancy, and post‐logging productivity in northern temperate and boreal forests","year":2018,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Ministère des Ressources naturelles et des Forêts; Université Laval; Université TÉLUQ; Centre de Géomatique du Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Ministère de l'Économie, de l’Innovation et des Exportations du Québec; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Ecology; Biodiversity; Logging; Ecosystem; Disturbance (geology); Forest ecology; Temperate rainforest; Forest management; Productivity; Forest restoration; Environmental science; Geography; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003391694,0.00006878372,0.00007963979,0.00002637996,0.0007103049,0.00001050915,0.00006065208,0.00005792416,0.00009712164],"category_scores_gemma":[0.00008998015,0.00005929884,0.00001016918,0.0001214441,0.000692916,0.00009163638,0.0005199344,0.00008786142,0.00007781972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006629367,"about_ca_system_score_gemma":0.000007521258,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006692965,"about_ca_topic_score_gemma":0.02192463,"domain_scores_codex":[0.9993432,0.00006967365,0.00009396976,0.0002947253,0.0000603033,0.0001381652],"domain_scores_gemma":[0.9996572,0.0001604926,0.00003562823,0.00008463767,0.00001757832,0.00004444423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007023614,0.00009699317,0.9975665,0.000001647605,0.000003994577,8.066653e-7,0.0001860747,0.00003023095,0.00045518,0.0007095349,0.00007031167,0.0008085102],"study_design_scores_gemma":[0.0001654773,0.0001109616,0.9890845,8.123574e-7,0.000004753377,0.000006623722,0.00004973096,0.0002809588,0.00001214905,0.009823652,0.0003904809,0.0000698718],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942515,0.00001364736,0.0003826553,0.003587665,0.00002031401,0.0002835226,0.000005799552,0.00002687665,0.001427956],"genre_scores_gemma":[0.9989238,0.000009021647,0.0004194141,0.0002024595,0.00002365274,0.0001227646,0.000005877961,0.000002551055,0.0002904383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0218577,"threshold_uncertainty_score":0.9959227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209003667636202,"score_gpt":0.2298121022573825,"score_spread":0.2177220655810205,"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."}}