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Record W7139529828

Wetlands

2007· article· W7139529828 on OpenAlexaboutno aff
Scott D. Bridgham, J. Patrick Megonigal, Norman B. Bliss, Carl Trettin

Bibliographic record

VenueChapman University Digital Commons (Chapman University) · 2007
Typearticle
Language
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTonneGreenhouse gasCarbon sinkSink (geography)Fossil fuelAtmosphere (unit)WetlandAtmospheric carbon cycleGreenhouse effect
DOInot available

Abstract

fetched live from OpenAlex

North America is currently a net source of CO2 to the atmosphere, contributing to the global buildup of greenhouse gases in the atmosphere and associated changes in the Earth's climate. In 2003, North America emitted nearly two billion metric tonnes of carbon to the atmosphere as CO2. North America's fossil-fuel emissions in 2003 (1856 million metric tonnes of carbon ±10% with 95% certainty) were 27% of global emissions. Approximately 85% of those emissions were from the USA, 9% from Canada, and 6% from Mexico. The combustion of fossil fuels for commercial energy (primarily electricity) is the single largest contributor, accounting for approximately 42% of North American fossil emissions in 2003. Transportation is the second largest, accounting for 31% of total emissions. There are also globally important carbon sinks in North America. In 2003, growing vegetation in North America removed approximately 500 million tonnes of carbon per year (±50%) from the atmosphere and stored it as plant material and soil organic matter. This land sink is equivalent to approximately 30% of the fossil-fuel emissions from North America. The imbalance between the fossil-fuel source and the sink on land is a net release to the atmosphere of 1350 million metric tonnes of carbon year (±25%). Approximately 50% of North America's terrestrial sink is due to the regrowth of forests in the USA on former agricultural land that was last cultivated decades ago, and on timberland recovering from harvest. Other sinks are relatively small and not well quantified with uncertainties of 100% or more. The future of the North American terrestrial sink is also highly uncertain. The contribution of forest regrowth is expected to decline as the maturing forests grow more slowly and take up less CO2 from the atmosphere. However, the response of regrowing forests and other sinks to climatic changes and CO2 concentration in the atmosphere is highly uncertain. The large difference between current sources and sinks and the expectation that the difference could become large if the growth of fossil-fuel emissions continues and land sinks decline suggest that addressing imbalances in the North American carbon budget will likely require actions focused on reducing fossil-fuel emissions. Options to enhance sinks (growing forests or sequestering carbon in agricultural soils) can contribute, but enhancing sinks alone is likely insufficient to deal with either the current or future imbalance. Options to reduce emissions include efficiency improvement, fuel switching, and technologies such as carbon capture and geological storage. Implementing these options will likely require an array of policy instruments at local, regional, national, and international levels, ranging from the encouragement of voluntary actions to economic incentives, tradable emissions permits, and regulations. Meeting the demand for information by decision makers will likely require new modes of research characterized by close collaboration between scientists and carbon management stakeholders.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.317
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3170.153

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 head0.011
GPT teacher head0.184
Teacher spread0.172 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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