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Record W2123197653 · doi:10.1029/2010eo260001

Arctic Landscapes in Transition: Responses to Thawing Permafrost

2010· article· en· W2123197653 on OpenAlexaff
J. C. Rowland, C. E. Jones, G. Altmann, Rorke B. Bryan, B. T. Crosby, L. D. Hinzman, D. L. Kane, David M. Lawrence, A. Mancino, Philip Marsh, J. P. McNamara, V. E. Romanvosky, Horacio Toniolo, B. J. Travis, Erin Trochim, Cathy J. Wilson, G. L. Geernaert

Bibliographic record

VenueEos · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsEnvironment and Climate Change Canada
FundersLos Alamos National LaboratoryInternational Arctic Research Center, University of Alaska, Fairbanks
KeywordsPermafrostTundraArcticErosionCoastal erosionWetlandClimate changePhysical geographySedimentThermokarstLandformGeologyArctic vegetationTree lineOceanographyEnvironmental scienceHydrology (agriculture)GeographyGeomorphologyEcology

Abstract

fetched live from OpenAlex

Observations indicate that over the past several decades, geomorphic processes in the Arctic have been changing or intensifying. Coastal erosion, which currently supplies most of the sediment and carbon to the Arctic Ocean [Rachold et al., 2000], may have doubled since 1955 [Mars and Houseknecht, 2007]. Further inland, expansion of channel networks [Toniolo et al., 2009] and increased river bank erosion [Costard et al., 2007] have been attributed to warming. Lakes, ponds, and wetlands appear to be more dynamic, growing in some areas, shrinking in others, and changing distribution across lowland regions [e.g., Smith et al., 2005]. On the Arctic coastal plain, recent degradation of frozen ground previously stable for thousands of years suggests 10–30% of lowland and tundra landscapes may be affected by even modest warming [Jorgenson et al., 2006]. In headwater regions, hillslope soil erosion and landslides are increasing [e.g., Gooseff et al., 2009].

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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.248
Teacher spread0.224 · 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

Citations295
Published2010
Admission routes1
Has abstractyes

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