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Record W2107447717 · doi:10.1111/ele.12164

Tundra ecosystems observed to be CO<sub>2</sub>sources due to differential amplification of the carbon cycle

2013· review· en· W2107447717 on OpenAlexafffund
E. Fay Belshe, Edward A. G. Schuur, Benjamin M. Bolker

Bibliographic record

VenueEcology Letters · 2013
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsTundraCarbon cycleEcosystemEnvironmental scienceEcologyCarbon fibersTerrestrial ecosystemEnvironmental chemistryDifferential (mechanical device)Atmospheric sciencesBiologyChemistryGeologyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Are tundra ecosystems currently a carbon source or sink? What is the future trajectory of tundra carbon fluxes in response to climate change? These questions are of global importance because of the vast quantities of organic carbon stored in permafrost soils. In this meta-analysis, we compile 40 years of CO2 flux observations from 54 studies spanning 32 sites across northern high latitudes. Using time-series analysis, we investigated if seasonal or annual CO2 fluxes have changed over time, and whether spatial differences in mean annual temperature could help explain temporal changes in CO2 flux. Growing season net CO2 uptake has definitely increased since the 1990s; the data also suggest (albeit less definitively) an increase in winter CO2 emissions, especially in the last decade. In spite of the uncertainty in the winter trend, we estimate that tundra sites were annual CO2 sources from the mid-1980s until the 2000s, and data from the last 7 years show that tundra continue to emit CO2 annually. CO2 emissions exceed CO2 uptake across the range of temperatures that occur in the tundra biome. Taken together, these data suggest that despite increases in growing season uptake, tundra ecosystems are currently CO2 sources on an annual basis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.071
GPT teacher head0.258
Teacher spread0.186 · 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.

Study designNot applicable
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

Citations190
Published2013
Admission routes2
Has abstractyes

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