Papers where the original authors have engaged with our plain-language explanation.
For every paper on this page, at least one of the original authors has seen our plain-language explanation and engaged with it — either confirming it reads accurately or requesting corrections that we then applied. An endorsement does not mean the authors formally approve every sentence, but it does mean the explanation has passed the eyes of the people who wrote the paper.
1335 papers reviewed by authors · 121–130 / 1335
By combining the first monthly-resolved fossil coral records from the last deglaciation with climate model simulations, this study demonstrates that El Niño events were weaker and less frequent in colder climates, thereby validating model projections that extreme El Niño events will become more frequent as the planet warms.
This paper presents a rapid, label-free antimicrobial susceptibility testing platform that integrates pH-responsive hydrogel-infiltrated porous silicon microchips with microfluidics to detect bacterial metabolic acidification via optical interferometry, enabling accurate antibiotic susceptibility results for *E. coli* within 1.5 hours.
This paper establishes a deterministic geometric criterion for the analytic continuation of the multi-valued ultra-radical across its convergence radius and demonstrates that only the principal branch maintains structural continuity and stability as parameters vary, unlike other branches which exhibit divergence in critical limits.
This paper presents a modular, operator-based visual analytics pipeline that transforms synthetic financial data into dynamic visualizations of systemic risk dynamics, treating visualization as an integral analytical stage rather than a post-processing step to enhance the exploration and communication of complex risk processes.
This paper proposes a novel, industry-agnostic valuation framework that addresses the limitations of standard methods for AI-driven firms by introducing a milestone-based real-options model, an AI-specific taxonomy, and an Analytic Hierarchy Process-derived success index to decompose uncertainty into auditable option-level assumptions, demonstrated through a single-firm case study.
This paper introduces two structure-free machine learning models that accurately predict pairwise preferences between compounds and targets by comparing chemical structures and protein sequences, achieving high accuracy in ranking without requiring conformational analysis or protein structure data.
This study elucidates the molecular mechanism by which human GCN1 senses ribosome collisions to activate the Integrated Stress Response, revealing that it rigidifies collided di-ribosomes and recruits the mammalian ortholog ABCF3 (the functional equivalent of yeast Gcn20) during amino acid starvation.
This paper introduces CAMELS-CROCODILE, a new suite of cosmological hydrodynamic simulations using the Gadget code and Osaka feedback model to provide an independent baryonic physics implementation for testing machine learning inference, demonstrating that models trained on distinct galaxy formation frameworks like IllustrisTNG fail to generalize accurately without retraining.
This paper argues that much of the performance gap in automated clinical coding attributed to model error actually stems from unmodeled, systematic coding styles, demonstrating that explicitly conditioning models on a coder-specific style rubric significantly improves accuracy and resolves discrepancies across different evaluation methods.
This study demonstrates that while mangrove wave attenuation in Colombia is primarily driven by volumetric density and hydrodynamic conditions, high local variability in these factors often masks broader regional differences across contrasting geomorphic settings.