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Record W2124470438 · doi:10.1080/10889370009377689

Hydrological and meteorological observations at Lake Tuborg, Ellesmere Island, Nunavut, Canada

2000· article· en· W2124470438 on OpenAlexaboutno aff
Carsten Braun, Douglas R. Hardy, Raymond S. Bradley, Michael J. Retelle

Bibliographic record

VenuePolar Geography · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsSnowmeltMeltwaterArcticSurface runoffSnowEnvironmental scienceSedimentHydrology (agriculture)PrecipitationWatershedStreamflowWater yearPermafrostClimate changeSlushPhysical geographyClimatologyOceanographyGeologyDrainage basinGeomorphologyMeteorologyGeographyEcology

Abstract

fetched live from OpenAlex

Abstract Hydrological and meteorological observations at Lake Tuborg, Ellesmere Island, Nunavut, Canada in 1995 are used to investigate contemporary water and sediment transport processes. Here we describe a new environmental data set for the High Arctic, where such data are scarce. The studied watershed (∼460 km2) ranges in elevation between 63 and ∼1900 m asl and is 88% covered by a lobe of the Agassiz Ice Cap. Streamflow and sediment transport were strongly associated with snowmelt runoff, whereas the direct influence of summer precipitation events was negligible. Snowmelt was primarily controlled by synoptic‐scale climatic processes. Two high‐magnitude pulses of meltwater and slush contributed a significant portion of the measured suspended sediment load to Lake Tuborg. Such events may be associated each year with snowmelt along the Agassiz Ice Cap margin. Additional years of data collection are needed to define the annual and inter‐annual variability of the sediment delivery system, particularly with respect to the relative importance of summer rainfall events. Runoff and sediment transport to Lake Tuborg are very likely to increase under climatic warming conditions.

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 score0.599
Threshold uncertainty score0.939

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)0.0620.000

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 head0.022
GPT teacher head0.200
Teacher spread0.178 · 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

Citations12
Published2000
Admission routes1
Has abstractyes

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