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Record W2611172674 · doi:10.1139/as-2016-0006

Impact of permafrost thaw on the turbidity regime of a subarctic river: the Sheldrake River, Nunavik, Quebec

2017· article· en· W2611172674 on OpenAlexafffundvenueabout
Maxime Jolivel, Michel Allard

Bibliographic record

VenueArctic Science · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern Studies
FundersNatural Sciences and Engineering Research Council of CanadaArcticNetUniversité Laval
KeywordsTurbidityThermokarstHydrology (agriculture)PermafrostDrainage basinEnvironmental scienceSnowmeltSubarctic climateErosionSedimentSnowGeologyOceanographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

In order to assess the impact of seasonal active layer thaw and thermokarst on river flow and turbidity, a gauging station was installed near the mouth of the Sheldrake River in the discontinuous permafrost zone of northern Quebec. The station provided 5 years of water level data and 3 years of turbidity data. The hydrological data for the river showed the usual high water stage occurring at spring snowmelt, with smaller peaks related to rain events in summer. Larger and longer turbidity peaks also occurred in summer in response to warm air temperature spells, suggesting that a large part of the annual suspension load was carried during midsummer turbidity peaks. Supported by geomorphological observations across the catchment area, the most plausible interpretation is that the rapid thawing of the active layer during warm conditions in July led to the activation of frostboils and triggered landslides throughout the river catchment, thus increasing soil erosion and raising sediment delivery into the hydrological network. These results indicate that maximum sediment discharge in a thermokarst-affected region may be predominantly driven by the rate of summer thawing and associated activation of erosion features in the catchment.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.348
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.292
Teacher spread0.238 · 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

Citations9
Published2017
Admission routes4
Has abstractyes

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