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Fluvial Impact of Extensive Active Layer Detachments, Cape Bounty, Melville Island, Canada

2009· article· en· W4255041870 on OpenAlexaffabout
Scott F. Lamoureux, Melissa J. Lafrenière

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsWatershedFluvialTurbiditySedimentHydrology (agriculture)CapeErosionTurbidity currentTurbiditeSediment transportGeologyChannel (broadcasting)Environmental scienceOceanographyGeographyGeomorphologyArchaeology

Abstract

fetched live from OpenAlex

Exceptional and persistent warm temperatures recorded during July 2007 at Cape Bounty, Melville Island, Canada (74°54′N, 109°35′W), resulted in rapid and deep active layer formation. The thickened active layer, together with up to 10.8 mm of rainfall in late July, resulted in widespread active layer detachments across the West watershed during 23–31 July. Mapping indicates that approximately 1.9% of the watershed was directly impacted by disturbances. By contrast, only two small detachments occurred in the adjacent East watershed.The immediate fluvial impact of the detachments was primarily in the form of abrupt, short-lived rises in river turbidity, along with a more gradual increase in discharge and overall turbidity. Sediment transport pulses resulted from the hydrological connection of major detachment slides, most of which were upslope from the main channel. The largest detachment dammed the river over a length of 200 m, and resulted in an upstream pond and prolonged increased sediment transport. In total, the increased sediment transport during the last week of July amounted to an estimated 44.3 Mg, or 18% of the seasonal yield. While the detachments had an immediate and substantial impact on river conditions, erosion of unstable material is likely to have a sustained impact on watershed fluxes in future years.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.321
Teacher spread0.293 · 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.

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

Citations16
Published2009
Admission routes2
Has abstractyes

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