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Record W7014357729

Particulate organic matter dynamics and degradation in Arctic fluvial systems

2022· dissertation· en· W7014357729 on OpenAlexaboutno aff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostThermokarstArcticTotal organic carbonCarbon fibersOrganic matterParticulatesDissolved organic carbonGlobal warming
DOInot available

Abstract

fetched live from OpenAlex

The Arctic is warming two to four times the rate of global average. The increase in air temperatures causes permafrost (i.e., perennially frozen ground) to thaw and release previously frozen organic carbon (OC) to the contemporary carbon cycle. Permafrost stores large amounts of organic carbon (~1300 ± 200 Pg), which equals up to half of the belowground OC globally. Re-mineralization of the released permafrost OC can add greenhouse gases (CO2, CH4) to the atmosphere enhancing climate warming. Gradual permafrost thaw happens when the active layer (i.e., the topmost layer of permafrost that thaws during summer months) deepens due to climate warming releasing largely dissolved organic carbon (DOC). On the contrary, in permafrost regions with high ground ice-content, permafrost thaw happens abruptly (i.e., thermokarst) as landscapes subside or collapse due to melting of ice. Abrupt permafrost thaw releases dominantly particulate organic carbon (POC). While degradation of DOC has been extensively studied in Arctic fluvial systems, degradation of POC is still poorly characterized. In this study, we investigate POC composition and degradation in two different areas: i) in the thaw streams draining abrupt permafrost thaw features, retrogressive thaw slumps (RTS), on the Canadian Peel Plateau, and ii) in the Kolyma River, which is one of the major Arctic rivers draining to the Arctic Ocean. We also study carbon dynamics and water chemistry parameters in lower order streams within the Kolyma watershed in two hydrologically distinct seasons: spring freshet and summer. We use macro(molecular) methods, pyrolysis – gas chromatography mass spectrometry and lipid biomarkers (n-alkanes, n-alkanoic acids), to analyse POC composition and degradation status. For further compositional analysis, we use carbon isotopes (δ13C, Δ14C). Additionally, we employ spatial data analysis and statistical modelling to characterize the watersheds and POC sources. Our results indicate that POC composition is seasonally dependent, and it defines biodegradability of POC. On the Peel Plateau, POC consists largely of aromatic moieties and includes petrogenic carbon that are not easily degradable. By contrast, Kolyma River POC degrades relatively fast during summer, when it is mostly of autochthonous sources. However, freshet POC, dominated by allochthonous POC, is not readily degradable. During freshet, DOC is susceptible to adsorption to particles and/or flocculation, potentially attenuating its climate impact. The lower order streams within the Kolyma River watershed react fast to increase in air temperatures during spring freshet with increased surface water temperatures and depletion in δ13C-POC, suggesting early onset of primary production. Changes in water temperature and δ13C-POC were not as pronounced in the Kolyma River. These results suggest that lower order streams may start primary production and POC degradation earlier in the season than the larger ones and thus, start emitting greenhouse gases earlier. The degraded POC is mostly autochthonous, and more studies are needed to investigate whether degradation of autochthonous POC may stimulate degradation of allochthonous or permafrost POC. These results highlight the heterogeneity of the Arctic fluvial networks and the differences in their response to climate warming.

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 score0.273
Threshold uncertainty score0.543

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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
Published2022
Admission routes1
Has abstractyes

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