Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
VenueScience · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
FundersSmithsonian Tropical Research InstituteDirectorate for Biological SciencesInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementAgencia Nacional de Investigación y DesarrolloInstituto de Investigación de Recursos Biológicos Alexander von HumboldtUniversity of AlbertaUniversidade Federal de Santa CatarinaUniversidade Federal da ParaíbaWorld Agroforestry CentreUniversität InnsbruckUniversitat Autònoma de BarcelonaFondo Nacional de Ciencia Tecnología e InnovaciónUniversidade Federal de Minas GeraisCentre de Coopération Internationale en Recherche Agronomique pour le DéveloppementAgroParisTechUniversidade de São PauloFundação de Amparo à Pesquisa do Estado do Rio Grande do SulInstituto Tecnológico de Costa RicaUniversidad de AntioquiaUniversidade Federal de PernambucoCentre National de la Recherche ScientifiqueConselho Nacional de Desenvolvimento Científico e TecnológicoUniversität für Bodenkultur WienDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of Wisconsin-MadisonYale UniversityUniversity of StirlingUniversity of MinnesotaUniversiteit UtrechtWageningen University and ResearchNational University of SingaporeDeutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-LeipzigInstitut de Recherche pour le DéveloppementUniversity of the Sunshine CoastUniversidade Federal do Rio Grande do SulConsejo Nacional de Ciencia y TecnologíaMinisterio de Ciencia e InnovaciónMinistry of Education, IndiaHSBC Bank USAUniversidade Federal de ViçosaKwame Nkrumah University of Science and TechnologyPrimate ConservationColorado Mesa UniversityUniversity of ConnecticutUniversidad Nacional Autónoma de MéxicoStichting het KronendakYale-NUS CollegeNational Science FoundationSmithsonian InstitutionSecretaría de Educación PúblicaClemson UniversityColby CollegeFundação de Amparo à Pesquisa do Estado de Minas Gerais
KeywordsBiodiversityEcological successionDeforestation (computer science)TropicsEcosystemAgroforestrySecondary forestBiomass (ecology)Old-growth forestTropical climateClimate changeEcologyEnvironmental scienceGeographyForest restorationForest ecologySpecies diversityBiology
Abstract
fetched live from OpenAlexTropical forests disappear rapidly because of deforestation, yet they have the potential to regrow naturally on abandoned lands. We analyze how 12 forest attributes recover during secondary succession and how their recovery is interrelated using 77 sites across the tropics. Tropical forests are highly resilient to low-intensity land use; after 20 years, forest attributes attain 78% (33 to 100%) of their old-growth values. Recovery to 90% of old-growth values is fastest for soil (<1 decade) and plant functioning (<2.5 decades), intermediate for structure and species diversity (2.5 to 6 decades), and slowest for biomass and species composition (>12 decades). Network analysis shows three independent clusters of attribute recovery, related to structure, species diversity, and species composition. Secondary forests should be embraced as a low-cost, natural solution for ecosystem restoration, climate change mitigation, and biodiversity conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
