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Record W4387706353 · doi:10.3897/aca.6.e109612

Vegetation communities and summer net ecosystem CO2 exchange on western Axel Heiberg Island, Canadian High Arctic

2023· article· en· W4387706353 on OpenAlexafffundabout
Theresa Gossmann, Christopher R. Omelon

Bibliographic record

VenueARPHA Conference Abstracts · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsQueen's University
FundersMcGill University
KeywordsEnvironmental scienceArcticEcosystemVegetation (pathology)Climate changeArctic vegetationCarbon sinkPhysical geographySoil carbonEnvironmental changeEcologyAtmospheric sciencesOceanographyGeographySoil waterSoil scienceGeologyTundra

Abstract

fetched live from OpenAlex

Climate change is expected to result in the Arctic transitioning from a carbon sink to a carbon source environment, with models predicting half of the carbon stock of the upper 3 m soil layer to be released by the year 2300 (van Huissteden and Dolman 2012). However, uncertainty in latitudinal warming and changes in Arctic ecosystem functions, such as gross carbon ecosystem exchange (GEE), are poorly understood, in part a reflection of a high variability in vascular plant community diversity that is dependent upon and sensitive to physiographic controls, such as soil moisture, topography, and seasonal active layer depth (Walker et al. 2005). This heterogeneity complicates assessments of carbon fluxes on a landscape scale and how they will change in the future (Shaver et al. 2007), especially given their sensitivity to local changes in climate, such as warming and higher rates of rainfall (Bintanja 2018, Bintanja and Andry 2017). As part of the creation of a long-term ecological and environmental monitoring program at the McGill Arctic Research Station at Expedition Fiord, western Axel Heiberg Island, field-based studies in 2021-2022 of plant surveys and summer net ecosystem CO 2 exchange monitoring were undertaken to: define the major vegetation communities; quantify and investigate CO 2 fluxes with chambers and their analogous biophysical variables; and upscale plot level CO 2 measurements to the landscape scale using high spatial resolution remote sensing data. define the major vegetation communities; quantify and investigate CO 2 fluxes with chambers and their analogous biophysical variables; and upscale plot level CO 2 measurements to the landscape scale using high spatial resolution remote sensing data. The Expedition Fiord area is recognized as a polar oasis, with high plant species richness existing within an environment of heterogeneous physiography. At the moment, five vegetation communities have been identified ( xeric dwarf shrub barren , xeric-mesic dwarf shub barren , mesic dwarf shrub tundra , cassiope heath , and sedge meadow ) that varied as a function of species diversity, percent cover, soil moisture, and net ecosystem carbon exchange. Barren vegetation communities having stronger respiration fluxes (i.e., carbon source environments) while more vegetated communities have stronger photosynthesis fluxes (i.e., carbon sink environments). Landcover classification revealed with high accuracy (79.3%) that barren ground and barren vegetation communities cover a much larger area compared to wetter habitats. Upscaling summer season measured carbon fluxes based on the landcover map revealed that Expedition Fiord is a carbon source environment, with an average efflux of +94.6 g CO 2 /day. Ongoing work focuses on the expansion of carbon flux and subsurface monitoring locations, as well as studies of soil carbon and microbial diversity across the different land cover classifications, which will help to better resolve how soil microorganisms, plant detritus, labile organic carbon, soil moisture, slope, aspect, and bedrock geology influence CO 2 fluxes throughout the summer season in this high Arctic setting.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.002

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 head0.071
GPT teacher head0.249
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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes3
Has abstractyes

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