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Record W4409229169 · doi:10.1017/jog.2025.24

Monitoring the physical processes driving the mass loss of Tapado Glacier, Dry Andes of Chile

2025· article· en· W4409229169 on OpenAlexaff
Álvaro Ayala, Benjamin Aubrey Robson, Shelley MacDonell, Christophe Kinnard, Sebastián Vivero, Eduardo Yáñez San Francisco, Nicole Schaffer, Alexis Segovia, Michał Pętlicki, Franco Retamal-Ramírez, Simone Schauwecker, Gino Casassa

Bibliographic record

VenueJournal of Glaciology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversité du Québec à Trois-RivièresCenter for Northern Studies
FundersAgencia Nacional de Investigación y Desarrollo
KeywordsGlacierGeologyPhysical geographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract We summarise 15 years of field and remote monitoring of Tapado Glacier in north-central Chile (30° S). Observations include meteorological records, direct mass balance measurements, uncrewed aerial vehicle surveys and tri-stereo satellite imagery for deriving high-resolution elevation changes. Frequent droughts and a significant warming trend of 0.29°C decade −1 since 1974 have caused a decrease in glacier surface albedo and an accelerated loss of glacier area and mass, particularly since the onset of the Chilean Megadrought in 2010, associated here with a 43% winter precipitation deficit. Geodetic estimates indicate increasingly more negative mass balance, varying from slightly negative before 2000 to −0.18 ± 0.35 m w.e. a −1 in 2000–12, −0.44 ± 0.11 m w.e. a −1 in 2012–20 and −0.75 ± 0.12 m w.e. a −1 after 2020. Glacier mass loss is associated with several morphological changes, such as increased penitente height, a larger total surface area of ice cliffs and supraglacial ponds over the debris-covered section and more frequent falls of snow and ice from marginal ice surrounding a steep area of exposed bedrock. Tapado Glacier exemplifies how glacier mass loss is driven by various processes, requiring multiple monitoring techniques, and highlights the accelerated changes of the Andes cryosphere.

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 score0.005
Threshold uncertainty score0.123

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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