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Focus on the impact of climate change on wetland ecosystems and carbon dynamics

2016· article· en· W2531644338 on OpenAlexaff
Lei Meng, Nigel T. Roulet, Qianlai Zhuang, Torben R. Christensen, Steve Frolking

Bibliographic record

VenueEnvironmental Research Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsWetlandEcosystemEnvironmental scienceTerrestrial ecosystemCarbon cycleClimate changeCarbon fluxEcologyEnvironmental resource managementAtmospheric sciencesGeologyBiology

Abstract

fetched live from OpenAlex

The renewed growth in atmospheric methane (CH4) since 2007 after a decade of stabilization has drawn much attention to its causes and future trends. Wetlands are the single largest source of atmospheric CH4. Understanding wetland ecosystems and carbon dynamics is critical to the estimation of global CH4 and carbon budgets. After approximately 7 years of CH4 related research following the renewed growth in atmospheric CH4, Environmental Research Letters launched a special issue of research letters on wetland ecosystems and carbon dynamics in 2014. This special issue highlights recent developments in terrestrial ecosystem models and field measurements of carbon fluxes across different types of wetland ecosystems. The 14 research letters emphasize the importance of wetland ecosystems in the global CO2 and CH4 budget.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.132
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.263
Teacher spread0.243 · 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 teacher head, 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

Citations38
Published2016
Admission routes1
Has abstractyes

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