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Record W2003503637 · doi:10.1139/x99-214

The changing landscape of Canada's western boreal forest: the current dynamics of permafrost

2000· article· en· W2003503637 on OpenAlexvenueaboutno aff
Dale H. Vitt, Linda A. Halsey, S. C. Zoltai

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostPeatAggradationBorealPhysical geographyTaigaBogClimate changeThermokarstGeologyEnvironmental scienceHoloceneEarth scienceHydrology (agriculture)EcologyGeomorphologyOceanographyGeographyForestryPaleontology

Abstract

fetched live from OpenAlex

This paper examines the impact that climatic change over the last millennium has had on aggradation and degradation of permafrost peatlands and the associated change in organic matter accumulation. Permafrost reached its southernmost Holocene extent in boreal continental western Canada during the Little Ice Age with 28 800 km 2 of permafrost peatland present within a sensitive zone demarcated by permafrost degradation. Subsequent degradation of permafrost has occurred in response to warming, with forested bogs changing to nonforested poor fens, associated with rising water levels. In conjunction with this ecosystem change, long-term net organic matter accumulation increases. As permafrost is in disequilibrium with climate, much of the permafrost that remains is in a relict state. Mapping of past and present permafrost distribution from peatland landforms indicates only 9% has degraded since the Little Ice Age, resulting in a 5% increase in long-term net organic matter accumulation. Of the permafrost that remains, 22% is in disequilibrium, located largely in the northern part of the sensitive zone. Additional loss of forested lands will occur in the future in boreal continental western Canada under present-day climatic conditions as permafrost approaches equilibrium, with a further 11% increase in long-term net organic matter accumulation predicted.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.533
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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 teacher head, 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

Citations126
Published2000
Admission routes2
Has abstractyes

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