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41 results for “tipping point”
Deep learning for the occurrence of tipping points: training data
<p>This data accompanies the manuscript by Chengzuo Zhuge et al. “Deep learning for the occurrence of tipping points” and the Github repository <a href="https://github.com/zhugchzo/dl_occurrence_tipping">https://github.com/zhugchzo/dl_occurrence_tipping</a>. It contains the model time series data that are used to train the deep learning algorithm. The directory <br>increased_bifurcation contains 150k time series (50k Fold, Hopf, Transcritical respectively) with parameter increasing and the directory decreased_bifurcation contains 150k time series (50k Fold, Hopf, Transcritical respectively) with parameter decreasing. The directory pitchfork contains 100k time series (50k supercritical and subcritical pitchfork respectively) with parameter increasing. Both directories contain files labels.csv and groups.csv which provide numbers corresponding to the labels (The tipping points) and groups (Training, Validation, Test) for each time series respectively.</p>
Deep learning for early warning signals of tipping points : supplementary data
<p>This data accompanies the publication by Bury et al. “Deep learning for early warning signals of tipping points” published in PNAS and the Github repository <a href="https://github.com/ThomasMBury/deep-early-warnings-pnas">https://github.com/ThomasMBury/deep-early-warnings-pnas</a>. It contains the model time series data that are used to train the deep learning algorithm. The directory ts_500 contains 500k time series used to train the 500-classifier. The directory ts_1500 contains 200k time series used to train the 1500-classifier. Both directories contain files <em>labels.csv </em>and <em>groups.csv </em>which provide numbers corresponding to the labels (Fold, Hopf, Branch, Null) and groups (Training, Validation, Test) for each time series respectively.</p>
Supplementary video of Petrini et al. 2023, submitted to TC. "Topographically-controlled tipping point for complete Greenland Ice Sheet melt"
<p>Animations showing the evolution of the Greenland Ice Sheet in simulations with different SMB and global mean temperature levels. </p>
Evaluation of Tipping Point
ClinicalTrials.gov study NCT03965273. IPD Sharing: YES. Countries: 1. Publications: 17.
Preventing Tipping Points in High Comorbidity Patients: A Lifeline From Health Coaches
ClinicalTrials.gov study NCT04176510. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A tipping-point in carbon storage when forest expands into tundra is related to mycorrhizal recycling of nitrogen
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Data from: Unveiling tipping points in long-term ecological records from Sphagnum-dominated peatlands
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Landscape structure affects metapopulation-scale tipping points
<p>Even when environments deteriorate gradually, ecosystems may shift abruptly from one state to another. Such catastrophic shifts are difficult to predict and sometimes to reverse (so-called hysteresis). While well studied in simplified contexts, we lack a general understanding of how catastrophic shifts spread in realistically spatially structured landscapes. For different types of landscape structures, including typical terrestrial modular and riverine dendritic networks, we here investigate landscape-scale stability in metapopulations whose patches can locally exhibit catastrophic shifts. We find that such metapopulations usually exhibit large-scale catastrophic shifts and hysteresis and that the properties of these shifts depend strongly on the metapopulation spatial structure and on the population dispersal rate: an intermediate dispersal rate, a low average degree or a riverine spatial structure can largely reduce hysteresis size. Our study suggests that large-scale restoration is easier with spatially clustered restoration efforts and in populations characterized by an intermediate dispersal rate.</p>
Supplementary Data for "Unveiling Geomagnetic Reversals: Insights from Tipping Points Theory"
<p>This file contains the data used in the study "Unveiling Geomagnetic Reversals: Insights from Tipping Points Theory".</p>
Landscape structure affects metapopulation-scale tipping points
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Data from: Social tipping points in animal societies in response to heat stress
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Molecular characterization of a toxicological tipping point in human induced pluripotent stem cell (hiPSC)-derived endoderm exposed to all-trans retinoic acid (ATRA)
GEO Series GSE131921. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing.
Data from: Avoiding tipping points in fisheries management through Gaussian process dynamic programming
Model uncertainty and limited data are fundamental challenges to robust management of human intervention in a natural system. These challenges are acutely highlighted by concerns that many ecological systems may contain tipping points, such as Allee population sizes. Before a collapse, we do not know where the tipping points lie, if they exist at all. Hence, we know neither a complete model of the system dynamics nor do we have access to data in some large region of state space where such a tipping point might exist. We illustrate how a Bayesian non-parametric approach using a Gaussian process (GP) prior provides a flexible representation of this inherent uncertainty. We embed GPs in a stochastic dynamic programming framework in order to make robust management predictions with both model uncertainty and limited data. We use simulations to evaluate this approach as compared with the standard approach of using model selection to choose from a set of candidate models. We find that model selection erroneously favours models without tipping points, leading to harvest policies that guarantee extinction. The Gaussian process dynamic programming (GPDP) performs nearly as well as the true model and significantly outperforms standard approaches. We illustrate this using examples of simulated single-species dynamics, where the standard model selection approach should be most effective and find that it still fails to account for uncertainty appropriately and leads to population crashes, while management based on the GPDP does not, as it does not underestimate the uncertainty outside of the observed data.
Data from: Generic indicators for loss of resilience before a tipping point leading to population collapse
Theory predicts that the approach of catastrophic thresholds in natural systems (e.g., ecosystems, the climate) may result in an increasingly slow recovery from small perturbations, a phenomenon called critical slowing down. We used replicate laboratory populations of the budding yeast Saccharomyces cerevisiae for direct observation of critical slowing down before population collapse. We mapped the bifurcation diagram experimentally and found that the populations became more vulnerable to disturbance closer to the tipping point. Fluctuations of population density increased in size and duration near the tipping point, in agreement with the theory. Our results suggest that indicators of critical slowing down can provide advance warning of catastrophic thresholds and loss of resilience in a variety of dynamical systems.
Committed global warming risks crossing critical thresholds for climate tipping points
<p>Scripts and data used to create results and figures for manuscript "Committed global warming risks crossing critical thresholds for climate tipping points"</p>
Tipping Point: Using Social Network Theory to Accelerate Scale and Impact
ClinicalTrials.gov study NCT05777473. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Avoiding tipping points in fisheries management through Gaussian process dynamic programming
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Data from: Generic indicators for loss of resilience before a tipping point leading to population collapse
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Tipping Point Biomarkers in Human Airway Cells
GEO Series GSE80733. Homo sapiens. 23 samples. Type: Expression profiling by array.
Fish Tail Point Tip XCB-105-1887
Fish Tail Point Tip. XCB-105-1887. 400 BCE-100 CE XCB-105 Adamagan (Aleut for place of walrus hunters) is at the head of Morzhovoi Bay, western Alaska Peninsula. It is a massive village with multiple occupations. When it was occupied 400 BCE-100 CE, it was the largest village in the Arctic with an estimated 1000 people. It also has limited occupations dated 2200-1700 BCE, 1000-600 BCE, and 900-1100 CE. The Western Alaska Peninsula artifacts are presented as a result of the research conducted under grants NSF 9630072, NSF 9814086, NSF 9996372, NSF 9996415, NSF 1139266, NSF 1321411. H. Maschner, Principal Investigator. These artifacts were scanned with either a Faro Edge Arm or a Minolta Vivid 9i. Processed in Geomagic or Polyworks. 2-8 photos were used for texture in Geomagic Wrap. Original digitizing work done at the IVL at Id. St. Univ. Subsequent processing and publication completed at Global Digital Heritage. Source: Objaverse 1.0 / Sketchfab
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