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Record W2027271127 · doi:10.1029/1999jc000068

Local and regional albedo observations of arctic first‐year sea ice during melt ponding

2001· article· en· W2027271127 on OpenAlexafffundabout
John Hanesiak, David G. Barber, R.A. De Abreu, John Yackel

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of CalgaryEnvironment and Climate Change CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaOffice of Naval ResearchUniversity of Manitoba
KeywordsAlbedo (alchemy)SnowEnvironmental scienceSea iceMelt pondArcticArctic ice packCryosphereGeologyMeltwaterAtmospheric sciencesShortwaveClimatologySea ice thicknessOceanographyRadiative transferGeomorphology

Abstract

fetched live from OpenAlex

The shortwave surface albedo is a critical climatological parameter for sea ice, especially during the spring melt period when the ice contains a mixture of highly reflective (snow) and absorptive (melt ponds) surfaces. Broadband and spectral albedo measurements were made over numerous surface types on first‐year sea ice during the melt period in Wellington Channel, Nunavut during the Collaborative‐Interdisciplinary Cryospheric Experiment 1997. Albedo measurements ranged from 0.75 (moist snow) to 0.21 (dark melt ponds) with many unique intermediate surfaces. Aircraft videography collected throughout Wellington Channel and Lancaster Sound was processed to reveal four main surface cover types (wet snow, mixed type, light ponds, and dark ponds). Surface albedo data were applied to the aircraft observations to upscale surface albedo measurements to regional scales. Aircraft video‐derived regional albedo (mean = 0.55±0.02) were comparable to helicopter and satellite‐derived (Advanced Very High Resolution Radiometer) albedo estimates.

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 score0.010
Threshold uncertainty score0.594

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.279
Teacher spread0.238 · 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

Citations85
Published2001
Admission routes3
Has abstractyes

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