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Record W3195230691 · doi:10.1038/s41586-021-03728-4

High aboveground carbon stock of African tropical montane forests

2021· article· en· W3195230691 on OpenAlexafffund
Aida Cuní‐Sanchez, Martin J. P. Sullivan, Philip J. Platts, Simon L. Lewis, Rob Marchant, Gérard Imani, Wannes Hubau, Iveren Abiem, Hari Adhikari, Tomáš Albrecht, Jan Altman, Christian Amani, Abreham Berta Aneseyee, Valerio Avitabile, Lindsay F. Banin, Rodrigue Batumike, Marijn Bauters, Hans Beeckman, Serge K. Begne, Amy C. Bennett, Robert Bitariho, Pascal Boeckx, Jan Bogaert, Achim Bräuning, Franklin Bulonvu, Neil D. Burgess, Kim Calders, Colin A. Chapman, Hazel Chapman, James A. Comiskey, Thalès de Haulleville, Mathieu Decuyper, Ben DeVries, Jiří Doležal, Vincent Droissart, Corneille E. N. Ewango, Senbeta Feyera, Aster Gebrekirstos, Roy E. Gereau, Martin Gilpin, Dismas Hakizimana, Jefferson S. Hall, Alan Hamilton, Olivier J. Hardy, Térese B. Hart, Janne Heiskanen, Andreas Hemp, Martin Herold, Ulrike Hiltner, David Hořák, Marie-Noel Kamdem, Charles Kayijamahe, David Kenfack, Mwangi James Kinyanjui, Julia A. Klein, Janvier Lisingo, Jon C. Lovett, Mark Lung, Jean-Remy Makana, Yadvinder Malhi, Andrew Marshall, Emanuel H. Martin, Edward T. A. Mitchard, A. Morel, John Tshibamba Mukendi, Tom Müller, Felix Nchu, Brigitte Nyirambangutse, Joseph Okello, Kelvin S.‐H. Peh, Petri Pellikka, Oliver L. Phillips, Andrew J. Plumptre, Lan Qie, Francesco Rovero, Moses N. Sainge, Christine B. Schmitt, Ondřej Sedláček, Alain Senghor K. Ngute, Douglas Sheil, Demisse Sheleme, Tibebu Yelemfrhat Simegn, Murielle Simo‐Droissart, Bonaventure Sonké, Teshome Soromessa, Trey Sunderland, Miroslav Svoboda, Hermann Taedoumg, James Taplin, David Taylor, Sean C. Thomas, Jonathan Timberlake, Darlington Tuagben, Peter M. Umunay, Eustrate Uzabaho, Hans Verbeeck, Jason Vleminckx, Göran Wallin, Charlotte Wheeler, Simon Willcock, John T. Woods, Etienne Zibera

Bibliographic record

VenueNature · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaUniversity of Guelph
FundersH2020 Marie Skłodowska-Curie ActionsNatural Environment Research CouncilConsortium of International Agricultural Research CentersEuropean CommissionMbarara University of Science and TechnologyMinistry of Environment - SaskatchewanGordon and Betty Moore FoundationLeverhulme TrustGrantová Agentura České RepublikyDeutsche ForschungsgemeinschaftSight Research UKNational Geographic SocietyUniversity of LeedsInternational Development Research Centre
KeywordsRainforestAgroforestryMontane ecologyReforestationGeographyForestryCarbon stockEcosystemEnvironmental scienceStock (firearms)EcologyClimate changeBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.004
GPT teacher head0.208
Teacher spread0.204 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations147
Published2021
Admission routes2
Has abstractno

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