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Effects of retrogressive permafrost thaw slumping on sediment chemistry and submerged macrophytes in Arctic tundra lakes

2010· article· en· W1554246042 on OpenAlexafffund
Patrícia Mesquita, Frederick J. Wrona, Terry D. Prowse

Bibliographic record

VenueFreshwater Biology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsImpactUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaUniversity of Victoria
KeywordsPermafrostMacrophyteTundraEnvironmental scienceBenthic zoneThermokarstSlumpingSedimentArcticHydrology (agriculture)Surface runoffEcologyGeologyOceanographyGeomorphology

Abstract

fetched live from OpenAlex

Summary 1. Global warming is predicted to cause changes in permafrost cover and stability in the Arctic. Zones of high ion concentration in regions of ice‐rich permafrost are a reservoir of chemicals that can potentially be transferred to fresh waters during thawing. Consequently, input of enriched runoff from the thaw and sediment and vegetation from the landscape could alter lakes by affecting their geochemistry and biological production. 2. Three undisturbed lakes and five lakes disturbed by retrogressive permafrost thaw slumps were sampled during late summer of 2006 to assess the potential effects of thermokarst shoreline slumping on water and sediment chemistry, the underwater light regime, and benthic macrophyte biomass and community structure. 3. Undisturbed lakes had sediments rich in organic material and selected micronutrients, while disturbed lakes had sediments richer in calcium, magnesium and strontium, greater transparency of the water column, and a well‐developed submerged macrophyte community. 4. It is postulated that enriched runoff chemistry may alter nutrient availability at the sediment–water interface and also the degradation of organic material, thus affecting lake transparency and submerged macrophytes. The results suggest that retrogressive permafrost slumping can significantly affect food webs in arctic tundra lakes through an increase in macrophyte biomass and development of a more complex benthic habitat.

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)0.000
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 score0.161
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.219
Teacher spread0.210 · 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

Citations69
Published2010
Admission routes2
Has abstractyes

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