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Record W4410632606 · doi:10.22215/etd/2025-16384

Evaluating annual carbon dioxide exchange between the atmosphere and shrub tundra in Canada’s Southern Arctic

2025· dissertation· en· W4410632606 on OpenAlexfundaboutno aff
Rachel Ruth Mandryk

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTundraShrubAtmosphere (unit)Carbon dioxideArcticThe arcticEnvironmental scienceCarbon dioxide in Earth's atmosphereClimatologyAtmospheric sciencesGeographyOceanographyMeteorologyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

The carbon cycle of Arctic tundra ecosystems is expected to change as the climate warms and shrub vegetation expands. However, there are few studies that have compared full year carbon fluxes over tundra with different shrub cover. In this study, soil respiration was measured year-round using forced-diffusion chambers and ecosystem-scale net ecosystem exchange of CO2 (NEE) was measured from spring through fall using eddy covariance towers at three Canadian tundra sites with varying dwarf birch shrub cover­. Soil respiration was larger at the site with most shrubs during most months except winter when soil CO2 emissions were similarly small at all three sites. Although there was more growing season net CO2 uptake at the site with more shrubs, relatively large emissions in spring and fall offset this sink strength so that all three sites were similarly small annual net sinks of CO2 (-19 to -31 g C m-2).

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.040
GPT teacher head0.271
Teacher spread0.231 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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