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Record W4416410072 · doi:10.1177/09596836251387253

Glacier retreat from the Little Ice Age to the year 2020 in the eastern sector of the Fuegian Andes, Tierra del Fuego, Argentina

2025· article· en· W4416410072 on OpenAlexaff
Juan Federico Ponce, Cristina Natalia San Martín, Brian Menounos, Andrea Coronato

Bibliographic record

VenueThe Holocene · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsGlacierGlacial periodCirqueMoraineWesterliesCirque glacierPrecipitationDeglaciationGlacier morphology

Abstract

fetched live from OpenAlex

We evaluate area change of small cirque glaciers in the Argentine sector of the Fuegian Andes, from moraines mapping, aerial photography (1970 and 1988), Landsat 5 (2000 and 2010), Landsat 8 (2015) and Sentinel (2020) satellite imagery. Changes in glacier area were compared to temperature and precipitation data from ERA5 over the period 1950–2020. Our results show a strong relation between glacial retreat and changes in air temperature. Collectively, the glaciers shrank by 70% from positions achieved during the Little Ice Age (~1870). Half of this glacial retreat (total glacier area loss equaled 3.8 km 2 ) occurred after 1960–1970 when an increase of approximately 1°C in the mean annual temperature was recorded in the area. We observe a rapid increase in the rate of area loss and glacial retreat speed after 2015, with average values of 0.010 km 2 yr −1 and 30 m/year, respectively, in concert with a rapid rise of observed air temperature (approximately 0.7°C). The intensification of the Southern Westerlies Winds (SWW) at this latitude, related to the positive trend in the Southern Annular Mode (SAM), produced an increase in temperature and decrease in precipitation in this sector which has induced a continuous and rapid glacial retreat since almost 1970.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.221
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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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