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Record W2890703539 · doi:10.1038/s41558-018-0283-x

Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks

2018· article· en· W2890703539 on OpenAlexaff
Giacomo Grassi, Joanna I. House, Werner A. Kurz, Alessandro Cescatti, R. A. Houghton, Glen P. Peters, María José Sanz, Raúl Abad Viñas, Ramdane Alkama, Almut Arneth, Alberte Bondeau, Frank Dentener, Marianela Fader, Sandro Federici, Pierre Friedlingstein, Atul K. Jain, Etsushi Kato, Charles D. Koven, Donna Lee, Julia E. M. S. Nabel, Alexander A. Nassikas, Lucia Perugini, Simone Rossi, Stephen Sitch, Nicolas Viovy, Andy Wiltshire, Sönke Zaehle

Bibliographic record

VenueNature Climate Change · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersEuropean CommissionSight Research UKDeutsche ForschungsgemeinschaftNatural Environment Research CouncilU.S. Department of EnergyNational Science Foundation
KeywordsComparabilityGreenhouse gasEnvironmental scienceLand use, land-use change and forestryClimate changeGlobal changeLand useCarbon sinkEnvironmental resource managementConceptual modelForest inventoryGlobal warmingNatural resource economicsForest managementAgroforestryComputer scienceEconomicsEcology

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)0.010
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.283
Teacher spread0.261 · 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 designSimulation or modeling
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

Citations236
Published2018
Admission routes1
Has abstractno

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