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Record W2095102607 · doi:10.1029/1999gb001243

Interannual variability of net ecosystem CO<sub>2</sub> exchange at a subarctic fen

2000· article· en· W2095102607 on OpenAlexaffabout
Timothy J. Griffis, Wayne R. Rouse, J. M. Waddington

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSubarctic climateEnvironmental scienceEcosystem respirationEcosystemCarbon sinkWetlandPhenologyGrowing seasonSink (geography)SnowmeltCarbon cycleAtmospheric sciencesClimatologyPrimary productionHydrology (agriculture)EcologyOceanographyGeographySurface runoffGeology

Abstract

fetched live from OpenAlex

Landscape‐scale net ecosystem CO 2 exchange (NEE) and the energy balance of a subarctic fen were studied during five growing seasons near Churchill, Manitoba. Interannual variability in NEE was large and ranged from a net sink of −235 g CO 2 m −2 in 1996 to a net source of +76 g CO 2 m −2 in 1994. Annual estimates of CO 2 exchange indicate that during the present period the fen is losing carbon nearly 3 times faster than its long‐term historical gain of about −11 g CO 2 m −2 yr −1 . Our estimates suggest that gross ecosystem photosynthesis may be more variable than ecosystem respiration on diurnal, seasonal, and interannual timescales. Our data strongly indicate that an early snowmelt combined with wet and warm conditions during the spring period lead to large carbon acquisition even when drier conditions were experienced over the majority of the growing season. The phenological stage of the vegetation relative to the climatic conditions experienced is an important cause of the interannual variability in NEE. An accurate representation of phenology in climate models is, therefore, critical to the success of forecasting the carbon budgets of northern wetlands.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.218
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

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

Citations142
Published2000
Admission routes2
Has abstractyes

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