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Record W2129243067 · doi:10.1002/2014jg002666

Phenology and its role in carbon dioxide exchange processes in northern peatlands

2014· article· en· W2129243067 on OpenAlexafffund
Angela Kross, Nigel T. Roulet, Tim R. Moore, Peter M. Lafleur, Elyn Humphreys, Jonathan Seaquist, Lawrence B. Flanagan, Mika Aurela

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsCarleton UniversityUniversity of LethbridgeTrent UniversityMcGill UniversityAgriculture and Agri-Food Canada
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaAcademy of FinlandEuropean CommissionCanadian Foundation for Climate and Atmospheric Sciences
KeywordsPhenologyEcosystemEnvironmental sciencePeatEcosystem respirationPrimary productionPrecipitationAtmospheric sciencesGrowing seasonCarbon cycleClimatologyEcologyGeographyMeteorologyBiology

Abstract

fetched live from OpenAlex

Abstract Ecosystem phenology plays an important role in carbon exchange processes and can be derived from continuous records of carbon dioxide (CO 2 ) exchange data. In this study we examined the potential use of phenological indices for characterizing cumulative annual CO 2 exchange in four contrasting northern peatland ecosystems. We used the approach of Jonsson and Eklundh (2004) to derive a set of phenological indices based on the daily time series of gross primary production (GPP), ecosystem respiration ( R e ), and net ecosystem production (NEP) measured in the four peatland sites. The main objectives of this study were (a) to examine the variation in phenological indices across sites and (b) to determine the relationships among phenological indices, environmental conditions, and cumulative annual CO 2 exchange. The phenological index used to define the “start of the growing season” showed good potential for differentiation among sites based on their average annual site GPP. Sites with earlier growing seasons had the highest average annual site GPP. The “peak CO 2 exchange rate” phenological index performed best in reflecting variations among sites and for estimating annual values of GPP, R e , and NEP (Pearson correlation coefficients ranged between 0.77 and 0.99, p < 0.05 for all.). The phenological indices and annual GPP, R e , and NEP were sensitive to winter (January–March) and summer (July–September) temperature and precipitation, but correlations, though significant, were weak.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.038
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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

Citations40
Published2014
Admission routes2
Has abstractyes

Explore more

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