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Record W2966530571 · doi:10.22215/etd/2018-12919

Spatial variability of carbon emissions within a drained lake basin and its surrounding tundra, Illisarvik, Northwest Territories

2018· dissertation· en· W2966530571 on OpenAlexaffabout
Andrée-Anne Laforce

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCarleton University
Fundersnot available
KeywordsTundraEnvironmental scienceCarbon dioxideVegetation (pathology)Greenhouse gasCarbon cycleMethaneStructural basinHydrology (agriculture)Carbon fibersAtmospheric methaneCarbon sinkPhysical geographyAtmospheric sciencesClimate changeEcologyGeologyGeographyEcosystemArcticOceanographyGeomorphology

Abstract

fetched live from OpenAlex

This study investigates the spatial and temporal variations of carbon emissions and their controls at a 38-year-old drained lake basin on Richards Island, NT.Greenhouse gas fluxes were collected from plots with different vegetation throughout the basin and in surrounding tundra during the growing season.Wet Sedge sites were significant sources of methane (7 to 355 nmol m -2 s -1 ) while most other sites were sinks.Carbon dioxide fluxes varied from 0.5 to 13 mol m -2 s -1 with highest fluxes outside the basin.Air temperature was positively correlated with carbon dioxide emissions at the majority of sites while soil moisture and vegetation type were the main controls on methane fluxes.Bulk age of the respired carbon dioxide was mostly modern, reflecting rapid cycling of recently sequestered carbon.Overall, carbon emissions were similar to those recorded at other tundra sites.This project could not have been possible without financial support of the Northern Scientific Training Program (NSTP), the Canadian Society for Agricultural and Forest Meteorology (CSAFM), the Ina Hutchison Award in Geography, Polar Continental Shelf Program grants to Chris Burn and Natural Science and Engineering Research Council of Canada Discovery grants to Elyn Humphreys and Chris Burn.I would like to thank Denis Granjon who positively influenced me and encouraged me to pursue this amazing field that is physical geography and Oliver Sonnentag who gave me the incredible opportunity to study abroad and who inspired me to pursue a Master's degree.I would also like to thank all my friends from Carleton grad studies for many good memories and for helping make the last two years incredible.Last but not least I am so grateful for my family and friends at home who showed me support throughout this journey, especially my parents who made this dream come true, merci.

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)0.000
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 score0.854
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.250
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 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

Citations2
Published2018
Admission routes2
Has abstractyes

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