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Record W2096597875 · doi:10.3189/002214307783258459

A new surface accumulation map for western Dronning Maud Land, Antarctica, from interpolation of point measurements

2007· article· en· W2096597875 on OpenAlexfundno aff
G. Rotschky, Per Holmlund, Elisabeth Isaksson, Robert Mulvaney, Hans Oerter, M. R. van den Broeke, Jan‐Gunnar Winther

Bibliographic record

VenueJournal of Glaciology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersEuropean Social FundInstitut Polaire Français Paul Emile VictorEuropean Science FoundationEuropean CommissionPolar Knowledge Canada
KeywordsGeologyFirnSnowInterpolation (computer graphics)Sampling (signal processing)Physical geographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract As a result of intensive field activities carried out by several nations over the past 15 years, a set of accumulation measurements for western Dronning Maud Land, Antarctica, was collected, based on firn-core drilling and snow-pit sampling. This new information was supplemented by earlier data taken from the literature, resulting in 111 accumulation values. Using Geographical Information Systems software, a first region-wide mean annual snow-accumulation field was derived. In order to define suitable interpolation criteria, the accumulation records were analyzed with respect to their spatial autocorrelation and statistical properties. The resulting accumulation pattern resembles well- known characteristics such as a relatively wet coastal area with a sharp transition to the dry interior, but also reveals complex topographic effects. Furthermore, this work identifies new high-return shallowdrilling sites by uncovering areas of insufficient sampling density.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.312
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations75
Published2007
Admission routes1
Has abstractyes

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