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Record W3081315084 · doi:10.1029/2020jg005679

Nutrient Uptake in the Supraglacial Stream Network of an Antarctic Glacier

2020· article· en· W3081315084 on OpenAlexaboutno aff
Anna Bergstrom, M. N. Gooseff, Joel Singley, Matthew J. Cohen, Kathleen A. Welch

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsGlacierSTREAMSNutrientHydrology (agriculture)GeologySedimentEnvironmental sciencePhysical geographyGeomorphologyEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract In polar regions, where many glaciers are cold based (frozen to their beds), biological communities on the glacier surface can modulate and transform nutrients, controlling downstream delivery. However, it remains unclear whether supraglacial streams are nutrient sinks or sources and the rates of nutrient processing. In order to test this, we conducted tracer injections in three supraglacial streams (62 to 123 m long) on Canada Glacier in the Taylor Valley, of the McMurdo Dry Valleys, Antarctica. We conducted a series of additions including nitrate (N), N + phosphate (P), N + P + glucose (C), and N + C. In two reaches, N‐only additions resulted in N uptake. The third reach showed net N release during the N‐only addition, but high N uptake in the N + P addition, indicating P‐limitation or N + P colimitation. Coinjecting C did not increase N‐uptake. Additionally, in these systems at low N concentrations the streams can be a net source of nitrogen. We confirmed these findings using laboratory‐based nutrient incubation experiments on sediment collected from stream channels on Canada Glacier and two other glaciers in the Taylor Valley. Together, these results suggest there is active biological processing of nutrients occurring in these supraglacial streams despite low sediment cover, high flow velocities, and cold temperatures, modifying the input signals to proglacial streams. As glaciers worldwide undergo rapid change, these findings further our understanding of how melt generated on glacier surfaces set the initial nutrient signature for subglacial and downstream environments.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.104
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.337
Teacher spread0.287 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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