ArticlePeer community journal2023
Article in Peer community journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025Article
- The genomic footprints of migration: how ancient DNA reveals our history of mobility.Genome biology · 2025Review
- Performance of qpAdm-based screens for genetic admixture on graph-shaped histories and stepping stone landscapes.Genetics · 2025Article
- Article
- Elucidating the sustainability of 700 y of Inuvialuit beluga whale hunting in the Mackenzie River Delta, Northwest Territories, Canada.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A general and efficient representation of ancestral recombination graphs.bioRxiv : the preprint server for biology · 2024Article
- disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.BMC bioinformatics · 2023Article
- disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.bioRxiv : the preprint server for biology · 2023Article
- Dispersal inference from population genetic variation using a convolutional neural network.Genetics · 2023Article
- Deep Learning in Population Genetics.Genome biology and evolution · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.