bohrium.com 的前十大競爭對手

scispace.com

From blogs, guides, and ebooks to product updates, expert insights, and interviews — A dedicated resource hub for students, librarians, researchers, and universities!

jenni.ai

Jenni is an AI research and academic writing assistant that helps students, researchers, and academics create essays, papers, and citations with ease.

consensus.app

These studies suggest that futures market information can improve spot market price forecasting accuracy, enhance traders' understanding of supply and demand, and lead spot prices, although their predictive power may vary under different market conditions and for different commodities.

scite.ai

Supporting: 66, Mentioning: 628 - Genomic, transcriptomic and proteomic profiles of human aneuploid cells reveal that mRNA levels increase with gene copy number, but protein levels are partially compensated. Aneuploid cells also exhibit common alterations in several pathways, including an activation of autophagy.

causaly.com

Causaly Copilot is powered by the most mature knowledge graph and the only scientific-grade information retrieval technology on the market.

paperpal.com

Struggling with your research paper introduction? Follow our proven workflows with real examples to write introductions that clearly present your research question and hook your readers.

semanticscholar.org

Quick guide to get started with Semantic Scholar Academic Graph API.

researchrabbit.ai

Struggling with literature reviews? Learn how to identify, compare, and connect different research paper types efficiently. Move beyond keywords with a connected approach to academic research.

elicit.com

Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.

arxiv.org

A system of non-tradable credits that flow between individuals like karma, hence proposed under that name, is a mechanism for repeated resource allocation that comes with attractive efficiency and fairness properties, in theory. In this study, we test karma in an online experiment in which human subjects repeatedly compete for a resource with time-varying and stochastic individual preferences or urgency to acquire the resource. We confirm that karma has significant and sustained welfare benefits even in a population with no prior training. We identify mechanism usage in contexts with sporadic high urgency, more so than with frequent moderate urgency, and implemented as a simple (binary) karma bidding scheme as particularly effective for welfare improvements: relatively larger aggregate efficiency gains are realized that are (almost) Pareto superior. These findings provide guidance for further testing and for future implementation plans of such mechanisms in the real world.