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Record W2011747948 · doi:10.1657/1938-4246-42.1.34

The Response of Taku and Lemon Creek Glaciers to Climate

2010· article· en· W2011747948 on OpenAlexaff
Alison S. Criscitiello, M. A. Kelly, Bruno Tremblay

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMcGill University
FundersNational Aeronautics and Space Administration
KeywordsGlacierTidewaterTidewater glacier cycleGlacier mass balanceGeologyClimate changePrecipitationClimatologyPhysical geographyGeomorphologyGeographyOceanographyMeteorology

Abstract

fetched live from OpenAlex

Surface height and mass balance changes of Taku and Lemon Creek Glaciers within Juneau Icefield, Alaska, are examined to determine the relationship between these parameters and climatic forcing. Both Taku and Lemon Creek Glaciers are located in a maritime climate, but they behave very differently. Taku Glacier, a former tidewater glacier, is ∼70 times larger than Lemon Creek Glacier, and its dynamics are largely a result of the post-tidewater glacier cycle which causes insensitivity to climate change during advance phases. Taku Glacier is advancing at present but its surface height, mass balance, and rate of advance have decreased since 1988. Lemon Creek Glacier, a small alpine glacier, is retreating and has maintained a negative mass balance since 1953. Mass balance records from both Taku and Lemon Creek Glaciers correlate well with temperature and show little correlation with precipitation. The mass balance of these glaciers also correlates with the Pacific Decadal Oscillation (PDO). However, the Lemon Creek Glacier mass balance record shows a stronger correlation with the PDO than that of Taku Glacier. Taku Glacier shows a longer delay in response to warming in Southeast Alaska likely due to post-tidewater glacier dynamics, its large accumulation area ratio (AAR), and its size.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.036
GPT teacher head0.310
Teacher spread0.274 · 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

Citations21
Published2010
Admission routes1
Has abstractyes

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