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Record W3137450742 · doi:10.13679/j.advps.2020.0033

Spatial variability in carbon dioxide exchange processes within wet sedge meadows in the Canadian High Arctic

2021· article· en· W3137450742 on OpenAlexaboutno aff
Claire Wright, A. Blaser, Paul Treitz, Neal A. Scott

Bibliographic record

VenueADVANCES IN POLAR SCIENCE · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsArcticEnvironmental scienceCarbon dioxidePhysical geographyAtmospheric sciencesEcologyGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Wet sedge meadows are the most productive plant communities in the High Arctic. However, the controls on carbon dioxide (CO2) exchange processes within wet sedge communities – and the scale at which they operate – are poorly understood. Here, the factors controlling CO2 exchange of wet sedge meadows experiencing different moisture regimes are examined. Environmental data are used to create predictive models of CO2 exchange on multiple temporal scales. Automated chamber systems recorded CO2 fluxes at 30-minute intervals at wet sedge sites in the Canadian High Arctic from June to August in 2014 and 2015. Static chambers were also deployed over a larger spatial extent in 2014. Our results show that wet sedge communities were strong CO2 sinks during the growing season (−7.67 to −44.36 g C•m−2). CO2 exchange rates in wetter and drier areas within wet sedge meadows differed significantly (Wilcoxon, p Citation: Wright C M, Blaser A C, Treitz P M, et al. Spatial variability in carbon dioxide exchange processes within wet sedge meadows in the Canadian High Arctic. Adv Polar Sci, 2021, 32(1): 1-19, doi: 10.13679/j.advps.2020.0033

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)0.003
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 score0.146
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.246
Teacher spread0.227 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueADVANCES IN POLAR SCIENCESame topicClimate change and permafrostFrench-language works237,207