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Record W1653493425

Modeling the Response of Glaciers to Climate Change in the Upper North Saskatchewan River Basin

2010· article· en· W1653493425 on OpenAlexaboutno aff
E. Booth, James Byrne, Hester Jiskoot, Ryan J. MacDonald

Bibliographic record

VenueAGUFM · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierStructural basinClimate changeGlacial periodWatershedDrainage basinStreamflowHydrology (agriculture)SnowClimatologyPrecipitationHydrological modellingPhysical geographyGeologyEnvironmental scienceGeographyGeomorphologyMeteorologyOceanographyCartography
DOInot available

Abstract

fetched live from OpenAlex

The objective of this M.Sc. research is to quantify historical and potential future impacts of climate change on glacial contribution to streamflow in the Upper North Saskatchewan River (UNSR) basin, Alberta, Canada. The physically-based Generate Earth SYstems Science input (GENESYS) hydro-meteorological model will be used to analyze the regional impacts of historical data, and to forecast future trends in the hydrology and climatology of selected watersheds within the basin. This model has recently been successfully applied to the St. Mary River watershed, Montana, and the UNSR basin (MacDonald et al. 2009; MacDonald et al. in press; Byrne et al. in review). Hydro-meteorological processes were simulated at high temporal and spatial resolutions over complex terrain, focusing on modeling snow water equivalent (SWE) and the timing of spring melt. A glacier mass balance model is currently in development for incorporation into GENESYS to more accurately gauge the effects of climate change on glaciated areas located in the UNSR basin. General Circulation Model (GCM) scenarios will be applied to develop meaningful projections of the range of future hydrologic change under reduced glacial conditions in the basin through 2100. ABSTRACT AND INTRODUCTION GLACIERS IN THE NORTH SASKATCHEWAN BASIN

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)0.001
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 score0.204
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.237
Teacher spread0.208 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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