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Record W2943221556 · doi:10.5194/essd-12-403-2020

Glaciers and climate of the Upper Susitna basin, Alaska

2020· article· en· W2943221556 on OpenAlexaff
Andrew Bliss, Regine Hock, G. J. Wolken, Erin Whorton, Caroline Aubry‐Wake, Juliana Braun, A. Gusmeroli, W. D. Harrison, Andrew O. Hoffman, Anna Liljedahl, Jing Zhang

Bibliographic record

VenueEarth system science data · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Saskatchewan
FundersAlaska Energy AuthorityColorado State University
KeywordsGlacierSnowStructural basinGlacier mass balancePermafrostGeologyPhysical geographyDrainage basinEnvironmental scienceClimate changeClimatologyHydrology (agriculture)GeomorphologyOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract. Extensive field observations were conducted in the Upper Susitna basin, a 13 289 km2 glacierized catchment in central Alaska in 2012–2014. This paper describes the comprehensive data set of meteorological, glacier mass balance, snow cover, and soil measurements, as well as the data collection and processing. Results are compared to similar observations from the 1980s. Environmental lapse rates measured with weather stations between about 1000 and 2000 m a.s.l. were significantly lower over the glaciers than the non-glacierized areas. Glacier-wide mass balances shifted from close to balanced in 1981–1983 to less than −1.5 m w.e. yr−1 in 2012–2014. Winter snow accumulation measured with ablation stakes on the glaciers closely matched observations from helicopter-borne radar. Soil temperature measurements across the basin showed that there was no permafrost in the upper 1 m of the soil column. The data produced by this study are available at: https://doi.org/10.14509/30138 (Bliss et al., 2019) and will be useful for hydrological and glaciological studies including modeling efforts.

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 score0.037
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.227
Teacher spread0.188 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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