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Tree-ring dating of colonized moraine surfaces in deglacierized areas of Greater Caucasus Mountains

2020· article· en· W3075105915 on OpenAlexaff
Olimpiu Pop, Ionela Georgiana Răchită, Daniel Germaın, Zurab Rikadze, Iulian‐Horia Holobâcă, Tamar Khuntselia, Mircea Alexe, Mariam Elizbarashvili, George Gaprindashvili, Kinga Ivan, Levan Tielidze

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMoraineGlacierChronologyDendrochronologyGeologyGlacial periodPhysical geographyTemperate climateDendroclimatologyClimate changeGeomorphologyArchaeologyGeographyPaleontologyOceanographyEcology

Abstract

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Since the end of the Little Ice Age (LIA) glacial advance, mountain glaciers of temperate zone have experienced an accelerated retreat accompanied by an increased production, transport and accumulation of glacial sediments. In these deglacierized areas, the development of a chronology for sediment deposition in the glacier forefronts remains challenging. Indeed, various dating methods are applicable but only few of these are capable to cover the last centuries with a high resolution. Amongst these methods, dendrochronological dating offers the possibility to reconstruct minimum ages of the moraines with a yearly resolution, providing a detailed chronology for glacier dynamics. Tree-ring dating relies on the assumption that the age of the oldest tree represents an estimate of the minimum age of the moraine resulting from the glacier movements. Although the Caucasus Range is one of the most heavily glaciated areas of temperate zone, field evidences and historical records point out that mountain glaciers are already in accelerated decline in response to climate warming since the LIA. In this respect, the main purpose of our study is to document historical changes of the Challaati glacier, located in Mestiachala river basin, over the last centuries by using tree-ring dating coupled with field survey investigations. The methodology involves the application of dendrochronology and geomorphological field mapping completed by GPS records. A total of 120 living Scots pine trees (Pinus sylvestris) growing on glacier forefield have been sampled with Pressler increment borers of various lengths. Tree-ring widths were measured with an accuracy of 0.01 mm using a LINTAB 5 measurement station (Rinntech, 2019). The quality of the visual cross-dating was statistically checked using the COFECHA program. In order to reduce uncertainties in dating the colonization age of moraines, various corrections were applied, including: (i) the reconstruction of the number of missing rings to the pith (pith offset estimation); (ii) the determination of age-height relationships for the study site (tree age estimation at the coring height corresponding with years a sapling needs to grow to breast height); and (iii) the determination of the ecesis, which is related to the period from the stabilization of the moraine surface to the germination and establishment of the first trees. Tree-ring analyses coupled with GPS records and geomorphological mapping of glacier forefield allowed us to reconstruct multiple stages of glacier recession, and also to calculate the retreat rates since the end of LIA. Therefore, this study highlights the usefulness of tree-ring dating coupled with field survey investigations to improve our knowledge and understanding of glacier forefield changes, but also to provide a robust dataset for the modelling the retreat of glaciers at various scales. This work represents a contribution to the joint research project ‘‘Impact du changement climatique sur les glaciers et les risques associés dans le Caucase géorgien (IMPCLIM)’’ co-funded by the Agence Universitaire de la Francophonie (AUF) and Institutul de Fizică Atomică (IFA), Romania.

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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.038
GPT teacher head0.245
Teacher spread0.207 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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