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Observing snowmelt dynamics on fast ice in Kongsfjorden, Svalbard, with NOAA/AVHRR data and field measurements

2009· article· en· W2140728223 on OpenAlex
Sascha Willmes, Jörg Bareiss, Christian Haas, Marcel Nicolaus

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolar Research · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Alberta
FundersNational Oceanic and Atmospheric AdministrationNorsk PolarinstituttNorges ForskningsrådDeutsche Forschungsgemeinschaft
KeywordsSnowmeltAdvanced very-high-resolution radiometerEnvironmental scienceClimatologySatelliteRemote sensingRadiometerArcticAtmospheric sciencesSnowMeteorologyGeologyOceanographyGeographyPhysics

Abstract

fetched live from OpenAlex

Temporal snowmelt dynamics on fast ice in Kongsfjorden/Svalbard are studied for the period 1990–2003, using visible and near-infrared channels of the Advanced Very High Resolution Radiometer (AVHRR). Long-term radiation data from an adjacent Baseline Surface Radiation Network station, as well as extensive glaciological and meteorological field measurements on the melting ice in 2002 and 2003, are used to validate a snowmelt index derived from the satellite data. This study shows that the remote sensing data are in good agreement with the field observations. However, the temporal variability of atmospheric water vapour has an impact on the snowmelt index, and must be accounted for through atmospheric correction. The analysis of long-term satellite data provides valuable insight into the strength and rate of the snowvolume decay, and reveals a strong interannual variability of the snowmelt intensity. However, a precise date for determining melt onset requires clear-sky AVHRR data throughout the onset period.

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.

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 score0.117
Threshold uncertainty score0.991

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 studies0.0000.000
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 head0.189
GPT teacher head0.350
Teacher spread0.161 · 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