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Record W2971690969 · doi:10.1017/jog.2019.60

Long-term observations of supraglacial streams on an Arctic glacier

2019· article· en· W2971690969 on OpenAlexafffundabout
Sarah L. St Germain, Brian J. Moorman

Bibliographic record

VenueJournal of Glaciology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Calgary
FundersParks CanadaNatural Sciences and Engineering Research Council of CanadaArctic Institute of North America
KeywordsGeologySinuosityCanyonSTREAMSGlacierMoraineGeomorphologyFluvialHydrology (agriculture)PermafrostChannel (broadcasting)Glacial periodStructural basinOceanography

Abstract

fetched live from OpenAlex

Abstract Supraglacial streams are a significant part of the glacial hydrological system and important for understanding glacial hydrology and dynamics. Here we infer factors that influence the long-term development of perennial supraglacial streams, particularly in reference to canyon, incised and surface stream formation. Orthophotos and digital elevation models generated from high-resolution aerial imagery taken with unmanned aerial vehicles or piloted helicopters between 2010 and 2017 were used to compare seven streams on Fountain Glacier, Bylot Island, Canada over time. Results show canyon formation occurs from a combination of fluvial processes and the impact of solar radiation. The greater the discharge or slope, the faster the incision and higher the sinuosity. With greater sinuosity, the distance between the top of the valley banks increases, and cutoffs cause trapezoidal canyon-like valleys to form. Solar radiation causes the backward migration of the valley walls, further enhancing canyon area. Canyons are less likely to occur in areas of low discharge and slope. Less incised channels are also more likely to have water flow jumping the channel banks, changing the channel path. The presence of medial moraines and crevasses also increases rerouting of small streams. Lastly, windblown created snow-plugs may lead to stream diversion.

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 score0.005
Threshold uncertainty score0.999

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)0.0020.000

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 head0.031
GPT teacher head0.257
Teacher spread0.225 · 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

Citations37
Published2019
Admission routes3
Has abstractyes

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