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Record W2422060443 · doi:10.1017/jog.2016.72

Calving of Fuerza Aérea Glacier (Greenwich Island, Antarctica) observed with terrestrial laser scanning and continuous video monitoring

2016· article· en· W2422060443 on OpenAlexaff
Michał Pętlicki, Christophe Kinnard

Bibliographic record

VenueJournal of Glaciology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersInstitut chilien de l'Antarctique
KeywordsIce calvingGlacierGeologyGlacier mass balancePhysical geographyGeomorphologyGeodesyGeography

Abstract

fetched live from OpenAlex

ABSTRACT A short-term series of quantitative observations of calving activity of Fuerza Aérea Glacier (Greenwich Island, the South Shetland Islands, Antarctica) was conducted in order to test new methods of monitoring calving. The volume of single calving events was quantified by combining terrestrial laser scanning (TLS) surveys with continuous video recording of the ice front. An empirical formula for area/volume scaling of the calved ice block was proposed based on the TLS measured calved ice volume and the calved ice front area obtained by manual delineation on the images acquired with the video camera. This combination of methods proves to be a valuable tool for glacier monitoring, providing both high-temporal resolution and precise quantitative measurements of the calving volume. The size distribution of calving events is best approximated by a power law and within the short period of observations (14 d) calving was found to be an intrinsic process not dependent on environmental forcings. Over the period of 21 January–04 February 2013 the ice flow velocity at the terminus of Fuerza Aérea Glacier was 0.26 ± 0.07 m d −1 and the calving rate was 0.41 ± 0.07 m d −1 .

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 score0.012
Threshold uncertainty score0.245

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 head0.028
GPT teacher head0.229
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

Citations34
Published2016
Admission routes1
Has abstractyes

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