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Record W2121149545 · doi:10.1139/x01-131

Comparing the carbon budgets of boreal and temperate deciduous forest stands

2002· article· en· W2121149545 on OpenAlexafffundvenueabout
Alan Barr, Timothy J. Griffis, T. Andrew Black, Xin-Yi Lee, Ralf M. Staebler, José D. Fuentes, Zengshan Chen, K. Morgenstern

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEddy covarianceEnvironmental scienceTemperate deciduous forestEcosystemDeciduousTemperate climateEcosystem respirationTaigaBorealTemperate rainforestTemperate forestBoreal ecosystemClimate changeCarbon cycleEvergreenEcologyAtmospheric sciencesBiology

Abstract

fetched live from OpenAlex

Boreal and temperate deciduous forests at northern mid-latitudes play an important role in the global carbon cycle. We analyze 3 years (1996-1998) of eddy-covariance carbon dioxide flux measurements from two contrasting deciduous forest ecosystems in the boreal and temperate regions of central Canada. The two forest stands have similar ages, heights, and leaf area indices but differ in species composition and climate. Mean annual net ecosystem productivity (NEP) was similar for the two ecosystems, varying between 0.7 and 2.7 t C·ha –1 (boreal) and 0.6 and 2.4 t C·ha –1 (temperate). In the boreal ecosystem, interannual differences in NEP were primarily controlled by early spring temperature. The warm spring of 1998 caused early leaf out and increased photosynthesis but had little effect on respiration. In the temperate ecosystem, the same warm spring not only caused early leaf out but also increased respiration and drought stress. The contrasting impact of the warm spring on annual NEP at the two sites illustrates the complexity of interpreting climatic impacts on the forest carbon balance. It also illustrates two competing influences of climate change on NEP: spring warming, which promotes photosynthesis and increases NEP, and increased soil temperature and drought, which promote respiration and reduce photosynthesis, thus reducing NEP. We discuss the need for a consistent data post-processing methodology in ecosystem intercomparisons. We also compare our results with a recent synthesis of data from European forests.

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 score0.371
Threshold uncertainty score0.973

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.254
Teacher spread0.217 · 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

Citations161
Published2002
Admission routes4
Has abstractyes

Explore more

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