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Landsat satellite images of the four soil landscapes showing: rocky uplands along Taylor Highway with stable surfaces subject to frequent fires; silty uplands near Hess Creek with mostly stable surfaces with occasional deep thermokarst lakes; gravelly–sandy lowlands on the Yukon Flats with widespread lake drainage associated with thawing permafrost; and peaty–silty lowlands on the Innoko Flats with widespread thermokarst

2013· article· en· W2341629436 on OpenAlexaboutno aff
Torre Jorgenson M, Y. Shur, Qianlai Zhuang, Kristen Manies, Ewing Stephanie, Wickland Kim, O'Donnell Jonathan, Mikhail Kanevskiy, Harden Jennifer, Robert G. Striegl

Bibliographic record

VenueFigshare · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsGeologySatelliteRemote sensingHydrology (agriculture)GeomorphologyArchaeologyGeographyGeotechnical engineering

Abstract

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Figure 2. Landsat satellite images of the four soil landscapes showing: rocky uplands along Taylor Highway with stable surfaces subject to frequent fires; silty uplands near Hess Creek with mostly stable surfaces with occasional deep thermokarst lakes; gravelly–sandy lowlands on the Yukon Flats with widespread lake drainage associated with thawing permafrost; and peaty–silty lowlands on the Innoko Flats with widespread thermokarst. Sampling locations shown as white dots. Abstract The diversity of ecosystems across boreal landscapes, successional changes after disturbance and complicated permafrost histories, present enormous challenges for assessing how vegetation, water and soil carbon may respond to climate change in boreal regions. To address this complexity, we used a chronosequence approach to assess changes in vegetation composition, water storage and soil organic carbon (SOC) stocks along successional gradients within four landscapes: (1) rocky uplands on ice-poor hillside colluvium, (2) silty uplands on extremely ice-rich loess, (3) gravelly–sandy lowlands on ice-poor eolian sand and (4) peaty–silty lowlands on thick ice-rich peat deposits over reworked lowland loess. In rocky uplands, after fire permafrost thawed rapidly due to low ice contents, soils became well drained and SOC stocks decreased slightly. In silty uplands, after fire permafrost persisted, soils remained saturated and SOC decreased slightly. In gravelly–sandy lowlands where permafrost persisted in drier forest soils, loss of deeper permafrost around lakes has allowed recent widespread drainage of lakes that has exposed limnic material with high SOC to aerobic decomposition. In peaty–silty lowlands, 2–4 m of thaw settlement led to fragmented drainage patterns in isolated thermokarst bogs and flooding of soils, and surface soils accumulated new bog peat. We were not able to detect SOC changes in deeper soils, however, due to high variability. Complicated soil stratigraphy revealed that permafrost has repeatedly aggraded and degraded in all landscapes during the Holocene, although in silty uplands only the upper permafrost was affected. Overall, permafrost thaw has led to the reorganization of vegetation, water storage and flow paths, and patterns of SOC accumulation. However, changes have occurred over different timescales among landscapes: over decades in rocky uplands and gravelly–sandy lowlands in response to fire and lake drainage, over decades to centuries in peaty–silty lowlands with a legacy of complicated Holocene changes, and over centuries in silty uplands where ice-rich soil and ecological recovery protect permafrost.

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: Dataset · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.074

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)0.0070.001

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 head0.034
GPT teacher head0.200
Teacher spread0.167 · 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
GenreDataset

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
Published2013
Admission routes1
Has abstractyes

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