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Record W652797751

Growing season carbon dioxide exchange of two contrasting peatland ecosystems

2005· dissertation· en· W652797751 on OpenAlexfundno aff
Aaron J. Glenn

Bibliographic record

VenueOpen ULeth Scholarship (OPUS) (University of Lethbridge) · 2005
Typedissertation
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Foundation for Climate and Atmospheric SciencesBIOCAP Canada
KeywordsPeatCarbon dioxideEnvironmental scienceEcosystemEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

The CO2 flux of two peatlands in northern Alberta was examined during the 2004 growing season using eddy covariance measurements of net ecosystem exchange (NEE), chamber measurements of total ecosystem respiration, and empirical models driven by meteorological inputs.The two ecosystems, a poor fen and an extreme-rich fen, differed significantly in plant species composition, leaf area index, aboveground biomass and surface water chemistry.The mean diurnal pattern of NEE at the peak of the season was similar between the sites, however, the extreme-rich fen had a higher photo synthetic and respiratory capacity than the poor fen.Over the 6 month study, the poor fen was shown to accumulate between 2 to 3 times more carbon than the extreme-rich fen despite having a lower photosynthetic capacity.The evergreen nature of the poor fen site allowed for a longer season of net CO2 uptake than the deciduous species that dominated the extreme-rich fen.iii v 4. Discussion 77 4.1 Seasonal trends in net ecosystem exchange 77 4.2 Peak growing season net ecosystem exchange 79 4.3 Chamber respiration measurements 82 4.4 Growing season carbon budget calculations 8 7 4.5 Estimation of uncertainty in net ecosystem exchange measurements 92 4.6 Implications offindings and potential future research 95 5. Conclusions 102

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)0.000
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: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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

Citations5
Published2005
Admission routes1
Has abstractno

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