{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008375169,0.0002306848,0.000172094,0.0006902793,0.0004543661,0.000582476,0.0002955869,0.0002380312,0.0006348278],"category_scores_gemma":[0.001380057,0.0001112919,0.0001793446,0.0006018996,0.0005485663,0.0003000351,0.0003209806,0.000215822,0.00005954204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002003939,"about_ca_system_score_gemma":0.001058224,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5771478,"about_ca_topic_score_gemma":0.7518253,"domain_scores_codex":[0.9998026,0.00003844754,0.00001183889,0.00004925386,0.00003292241,0.00006499712],"domain_scores_gemma":[0.998913,0.0003355418,0.0002892115,0.00005332069,0.0001552596,0.0002536046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005946662,0.00002588783,0.9956689,0.000008277484,0.00003336904,0.00003635077,0.0002178431,0.0003534736,0.001065135,0.00003373999,0.00004227296,0.002455298],"study_design_scores_gemma":[4.000962e-7,0.000005460134,0.999631,0.000001044387,0.000001906984,0.00000943908,0.00006990964,0.0002362215,0.00001517077,0.000007409129,0.00002083257,0.000001065665],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996419,0.00006433872,0.00005380703,0.000008308169,4.544781e-7,0.000001509185,0.000101428,0.000002264529,0.0001259901],"genre_scores_gemma":[0.999719,0.00001922607,0.00005780666,0.000005079286,8.103654e-7,0.000001840745,0.0001218046,6.477824e-7,0.00007374319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5771478,"threshold_uncertainty_score":0.8506848,"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."}}