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Record W2160569314 · doi:10.3189/172756411795931688

Observing regional-scale pack-ice decay processes with helicopter-borne sensors and moored upward-looking sonars

2011· article· en· W2160569314 on OpenAlexafffund
S.J. Prinsenberg, Ingrid Peterson

Bibliographic record

VenueAnnals of Glaciology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersCanadian Space AgencyArcticNet
KeywordsArctic ice packGeologySea iceClimatologyScale (ratio)ArcticSatelliteIce divideDrift iceRemote sensingEnvironmental scienceMeteorologyOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract The variability of Arctic pack-ice parameters (e.g. extent and ice type) has been monitored by satellite-borne sensors since the early 1960s, and information on ice thickness is now becoming available from satellite altimeters. However, the spatial resolution of satellite-derived ice properties is too coarse to validate fine-scale ice variability generated by regional-scale interaction processes that affect the coarse-scale pack-ice albedo, strength and decay. To understand these regional processes, researchers rely on other data-monitoring platforms such as moored upward-looking sonars and helicopter-borne sensors. Backed by observations, two such regional-scale pack-ice decay processes are discussed: the break-up of large pack-ice floes by long-period waves generated by distant storms, and the spring decay of first-year-ice ridges in a diverging pack-ice environment. These two processes, although occurring on regional spatial scales, are important contributors to the evolution of the total pack ice and need to be included in global climate models, especially as the conditions for their occurrence will alter due to climate change.

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 score0.008
Threshold uncertainty score0.515

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 head0.056
GPT teacher head0.247
Teacher spread0.191 · 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

Citations33
Published2011
Admission routes2
Has abstractyes

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