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Record W1973282065 · doi:10.5194/tc-8-1161-2014

MODIS observed increase in duration and spatial extent of sediment plumes in Greenland fjords

2014· article· en· W1973282065 on OpenAlexfundno aff
B. Hudson, Irina Overeem, Daniel McGrath, James P. M. Syvitski, A. B. Mikkelsen, Bent Hasholt

Bibliographic record

Venue˜The œcryosphere · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersQueen's UniversityUniversity of TwenteUniversiteit UtrechtUniversity of BristolQueen's University BelfastNational Science Foundation
KeywordsFjordGreenland ice sheetPlumeSedimentOceanographyGeologyMeltwaterFluvialGlacierPhysical geographyEnvironmental scienceIce sheetGeomorphologyStructural basinGeographyMeteorology

Abstract

fetched live from OpenAlex

Abstract. The freshwater flux from the Greenland Ice Sheet (GrIS) to the North Atlantic Ocean carries extensive but poorly documented volumes of sediment. We develop a suspended sediment concentration (SSC) retrieval algorithm using a large Greenland specific in situ data set. This algorithm is applied to all cloud-free NASA Moderate Resolution Imaging Spectrometer (MODIS) Terra images from 2000 to 2012 to monitor SSC dynamics at six river plumes in three fjords in southwest Greenland. Melt-season mean plume SSC increased at all but one site, although these trends were primarily not statistically significant. Zones of sediment concentration > 50 mg L−1 expanded in three river plumes, with potential consequences for biological productivity. The high SSC cores of sediment plumes ( > 250 mg L−1 expanded in one-third of study locations. At a regional scale, higher volumes of runoff were associated with higher melt-season mean plume SSC values, but this relationship did not hold for individual rivers. High spatial variability between proximal plumes highlights the complex processes operating in Greenland's glacio–fluvial–fjord systems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.870

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)0.0000.000

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 head0.017
GPT teacher head0.203
Teacher spread0.186 · 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

Citations61
Published2014
Admission routes1
Has abstractyes

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