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Record W1936701007 · doi:10.1139/as-2015-0003

Carbon exchange over four growing seasons for a subarctic sedge fen in northern Manitoba, Canada

2015· article· en· W1936701007 on OpenAlexafffundvenueabout
K. L. Hanis, B. D. Amiro, Mario Tenuta, Tim Papakyriakou, Kyle Swystun

Bibliographic record

VenueArctic Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaChurchill Northern Studies CentreArcticNet
KeywordsSubarctic climatePeatEddy covarianceEnvironmental scienceEcosystem respirationEcosystemGreenhouse gasCarbon dioxideCarbon fibersWetlandHydrology (agriculture)SnowBogMethaneCarbon cycleAtmospheric sciencesEcologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Net ecosystem exchange of carbon was measured using eddy covariance for four growing seasons at a subarctic hummocky fen in northern Manitoba, Canada. Over a 115 day measurement period each year, cumulative net ecosystem exchange of carbon ranged from a gain of 49 g C m −2 to a loss of 16 g C m −2 with a mean loss of 6 g C m −2 from the fen, with an uncertainty of about ±34 g C m −2 . Ecosystem respiration decreased with higher water tables (r 2 = 0.3), especially in one summer when flooding occurred to 0.12 m above the peat surface. Additional methane emissions previously documented for the site of 4–5.7 g C m −2 year −1 added to the carbon loss. Carbon loss was measured from this same fen in the 1990s and it is likely that the carbon gain (peat accumulation) during past centuries has not continued in recent decades. Scaling to annual greenhouse gas emissions as a 100 year global warming potential showed that this fen is currently a source of 192–490 g CO 2 -equivalents m −2 year −1 based on both carbon dioxide and methane flux measurements, indicating that peat is decomposing.

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 score0.245
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.228
Teacher spread0.200 · 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
Published2015
Admission routes4
Has abstractyes

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