{"id":"W3087670157","doi":"10.1038/s41467-020-18612-4","title":"Estimating retention benchmarks for salvage logging to protect biodiversity","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of Alberta; Royal Alberta Museum","funders":"National Tsing Hua University","keywords":"Logging; Salvage logging; Biodiversity; Species richness; Disturbance (geology); Rarefaction (ecology); Threatened species; Ecology; Habitat; Global biodiversity; Geography; Agroforestry; Biology; Snag","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001496524,0.00006578829,0.00006038612,0.000024971,0.0003889553,0.00003175465,0.0007269949,0.00007046023,0.000408514],"category_scores_gemma":[0.0002223028,0.00006721227,0.0000453202,0.0002563428,0.00005498953,0.0001277453,0.0007349777,0.0002500833,0.0003854281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004192429,"about_ca_system_score_gemma":0.000003284302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009004016,"about_ca_topic_score_gemma":0.0001200134,"domain_scores_codex":[0.9995067,0.00003459219,0.00009021899,0.0001470186,0.00009424148,0.0001272759],"domain_scores_gemma":[0.9993357,0.00005461722,0.00004776322,0.0004804838,0.000008146277,0.00007325346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002856318,0.00005954855,0.03392516,0.00004362652,0.00002392005,5.365416e-7,0.001985177,0.002591453,0.002068043,0.003478812,0.9456517,0.01014354],"study_design_scores_gemma":[0.0003504771,0.0001177645,0.02358055,0.00002204587,0.00004842215,5.407641e-7,0.00008059134,0.1025715,0.0001992667,0.0005150378,0.872221,0.0002928574],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.243061,0.0005692859,0.03277922,0.4915888,0.0006813853,0.008945596,0.0003600397,0.0008501558,0.2211645],"genre_scores_gemma":[0.8824684,0.000004556463,0.1137607,0.003403419,0.00003510534,0.00005230092,0.000165605,0.000004325063,0.0001055832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6394074,"threshold_uncertainty_score":0.4954028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03359254099083719,"score_gpt":0.2858188234110849,"score_spread":0.2522262824202477,"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."}}