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Record W1976529257 · doi:10.2480/agrmet.61.131

Effect of Soil Water Content on Carbon Dioxide Flux at a Sparse-Canopy Forest in the Canadian Boreal Ecosystem

2005· article· en· W1976529257 on OpenAlexaffabout
Hirokazu IWASHITA, Nobuko Saigusa, Shohei Murayama, Harry McCaughey, Andy Black, Alan Barr, Kaz Higuchi, Susumu Yamamoto

Bibliographic record

VenueJournal of Agricultural Meteorology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsQueen's UniversityUniversity of British ColumbiaEnvironment and Climate Change Canada
FundersNational Institute of Advanced Industrial Science and Technology
KeywordsEddy covarianceEcosystem respirationEnvironmental scienceEcosystemWater contentCanopySoil respirationTaigaBoreal ecosystemBorealHydrology (agriculture)Atmospheric sciencesSoil waterSoil scienceEcologyForestryGeographyGeologyBiology

Abstract

fetched live from OpenAlex

The relationship between soil moisture and ecosystem-level carbon budget was investigated for a BERMS (Boreal Ecosystem Research and Monitoring Sites) site in Saskatchewan, Canada, using eddy covariance flux measurements obtained during 2001 and 2002. The site was located in a young jack pine stand (Pinus banksiana Lamb.) characterized by a sparse low canopy. The site was harvested in 1994 and young trees from ages one year to ten years were regenerating naturally. Total ecosystem respiration (Rec) at the site was sensitive to volumetric soil water content (VWC), and increased exponentially with VWC and soil temperature in warm seasons. Under daytime light-saturated conditions, gross primary production (GPP) had a positive linear correlation with VWC, with enhancement occurring under high soil temperatures. In contrast to GPP and Rec individually, net ecosystem CO2 exchange (NEE) was less sensitive to VWC. Under VWC > 0.03, the VWC-dependence was hardly detectable for NEE, irrespective of temperature range.

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 score0.707
Threshold uncertainty score0.987

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations8
Published2005
Admission routes2
Has abstractyes

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