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Record W2057304220 · doi:10.3189/172756406781811376

Surface albedo observations of Hudson Bay (Canada) landfast sea ice during the spring melt

2006· article· en· W2057304220 on OpenAlexafffundabout
Jens K. Ehn, Mats A. Granskog, Tim Papakyriakou, R. J. Galley, David G. Barber

Bibliographic record

VenueAnnals of Glaciology · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of ManitobaCanada Research ChairsArcticNetChurchill Northern Studies CentreAcademy of FinlandManitoba Hydro
KeywordsAlbedo (alchemy)SnowSnowpackSea iceGeologyCryosphereIce-albedo feedbackClimatologyAtmospheric sciencesMeltwaterEnvironmental scienceMelt pondAntarctic sea iceGeomorphology

Abstract

fetched live from OpenAlex

Abstract The shortwave albedo is a major component in determining the surface energy balance and thus the evolution of the spring melt cycle. As the melt commences, the ice is partitioned into multiple surface types ranging from highly reflective white ice to absorptive blue ice. The reflectance from these surfaces shows significant spatial and temporal variability. Spectral albedo measurements were made at six different sites encompassing these two surface types, from 19 March to 3 May 2005, on 1.5 m thick landfast sea ice in southwestern Hudson Bay, Canada (58˚ N). Furthermore, the broadband albedo and the surface energy balance were continuously recorded at a nearby site during the 1 month period. Rapid changes in the albedo were found to relate to typical subarctic climate conditions, i.e. frequent incursions of southerly air, resulting snow and rain events and the generally high maximum solar insolation levels. Subsequently, diurnal variations in snow surface temperature were evident, often causing daytime melting and night-time refreezing resulting in the formation of ice lenses and superimposed ice. After rain events and extensive melting, the snowpack was transformed throughout into melt/freeze metamorphosed snow and superimposed ice. The integrated (350–1050 nm) albedo varied between 0.52 and 0.95 at the blue-ice sites, while it varied between 0.73 and 0.91 at white-ice sites. Variability on the order of ±10% in the white-ice broadband albedo resulted from the diurnal freeze–thaw cycle, but also synoptic weather events, such as snowfall and rain events, could rapidly change the surface 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.568

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)0.0000.000

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 head0.027
GPT teacher head0.226
Teacher spread0.200 · 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

Citations23
Published2006
Admission routes3
Has abstractyes

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