English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A novel method for automated annual layer counting in seasonally-resolved paleoclimate records has been developed. It relies on algorithms from the statistical framework of hidden Markov models (HMMs), which originally was developed for use in machine speech recognition. The strength of the layer detection algorithm lies in the way it is able to imitate the manual procedures for annual layer counting, while being based on statistical criteria for annual layer identification. The most likely positions of multiple layer boundaries in a section of ice core data are determined simultaneously, and a probabilistic uncertainty estimate of the resulting layer count is provided, ensuring an objective treatment of ambiguous layers in the data. Furthermore, multiple data series can be incorporated and used simultaneously. In this study, the automated layer counting algorithm has been applied to two ice core records from Greenland: one displaying a distinct annual signal and one which is more challenging. The algorithm shows high skill in reproducing the results from manual layer counts, and the resulting timescale compares well to absolute-dated volcanic marker horizons where these exist

An automated approach for annual layer counting in ice cores