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Record W2055398859 · doi:10.1016/j.yqres.2011.11.003

Frequency and intensity of high-altitude floods over the last 3.5 ka in northwestern French Alps (Lake Anterne)

2011· article· en· W2055398859 on OpenAlexaff
Charline Giguet‐Covex, Fabien Arnaud, Dirk Enters, Jérôme Poulenard, Laurent Millet, Pierre Francus, Fernand David, Pierre-Jérôme Rey, Bruno Wilhelm, Jean‐Jacques Delannoy

Bibliographic record

VenueQuaternary Research · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsFlood mythPrecipitationGeologyHoloceneErosionPhysical geographyPeriod (music)SedimentationNatural (archaeology)SedimentHydrology (agriculture)Altitude (triangle)GeomorphologyOceanographyArchaeologyPaleontologyGeography

Abstract

fetched live from OpenAlex

In central Western Europe, several studies have shown that colder Holocene periods, such as the Little Ice Age, also correspond to wet periods. However, in mountain areas which are highly sensitive to erosion processes and where precipitation events can be localized, past evolution of hydrological activity might be more complicated. To assess these past hydrological changes, a paleolimnological approach was applied on a 13.4-m-long sediment core taken in alpine Lake Anterne (2063 m asl) and representing the last 3.5 ka. Lake sedimentation is mainly composed of flood deposits triggered by precipitation events. Sedimentological and geochemical analyses show that floods were more frequent during cold periods while high-intensity flood events occurred preferentially during warmer periods. In mild temperature conditions, both flood patterns are present. This underlines the complex relationship between flood hazards and climatic change in mountain areas. During the warmer and/or dryer times of the end of Iron Age and the Roman Period, both the frequency and intensity of floods increased. This is interpreted as an effect of human-induced clearing for grazing activities and reveals that anthropogenic interferences must be taken into account when reconstructing climatic signals from natural archives.

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 score0.063
Threshold uncertainty score0.126

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 communication0.0010.000
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 head0.068
GPT teacher head0.297
Teacher spread0.229 · 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".

Quick stats

Citations78
Published2011
Admission routes1
Has abstractyes

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