A searchable index of real negative results, null findings, and replication failures from the published literature — so you can learn what didn't work before repeating it.
WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record (title, authors, DOI) compiled from open scholarly databases, with the abstract shown in full only where the paper is openly licensed (e.g. Creative Commons); otherwise a short excerpt is shown for reference under fair use. WASTE classifies each work by failure type; classifications are automated and approximate.
20253 results in Negative / Null Result Report · page 448 of 676
Gülise Gökce · 2025 · Brazilian Journal of Business
Following the September 11, 2001 attacks, the concept of "failed states" quickly gained prominence in international security literature, particularly under the influence of the United States. The 2002 U.S. National Security Strategy…
Angelo Di Porzio, Marco Coraggio · 2025 · arXiv
The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities -- such as rehabilitation therapy, sports, and manufacturing -- is expected to increase as these technologies become more pervasive. Designing cognitive architectures and control strategies to drive these agents requires realistic models of human motion. Furthermore, recent research has shown that each person exhibits a unique velocity signature, highlighting how individual motor behaviors are both rich in variability and internally consistent. However, existing models only provide simplifie
Daniel I. Rhon, Tina A. Greenlee, Rachel Mayhew et al. · 2022 · Journal of the American Academy of Orthopaedic Surgeons
Introduction: After elective orthopaedic surgery, many individuals go on to become long-term opioid users. Mitigating this risk has become a priority for surgeons, other members of the medical care team, and healthcare systems. The purpose…
Tianjiao Cao, Jiahao Lyu, Weichao Zeng et al. · 2025 · arXiv
Scene text detection has seen the emergence of high-performing methods that excel on academic benchmarks. However, these detectors often fail to replicate such success in real-world scenarios. We uncover two key factors contributing to this discrepancy through extensive experiments. First, a \textit{Fine-tuning Gap}, where models leverage \textit{Dataset-Specific Optimization} (DSO) paradigm for one domain at the cost of reduced effectiveness in others, leads to inflated performances on academic benchmarks. Second, the suboptimal performance in practical settings is primarily attributed to the
Christopher Brady, Xu Wu · 2025 · arXiv
The Organization for Economic Cooperation and Development (OECD) Working Party on Nuclear Criticality Safety (WPNCS) proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian Inverse Uncertainty Quantification (IUQ) as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of Generalized Linear Least Squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed
Yu-Han Huang · 2024 · Roadsides
This essay scrutinizes the soil-cement brick (SCB), a half-earthen, half-concrete building material, and its use in U.S.-aided housing projects in Cold War-era Taiwan. Made of cement and natural earth with manually operated ‘brickmaker’…
Meimingwei Li, Yuanhao Ding, Esteban Garces Arias et al. · 2026 · arXiv
Recent work has identified a counterintuitive phenomenon termed "Hyperfitting", where fine-tuning Large Language Models (LLMs) to near-zero training loss on small datasets surprisingly enhances open-ended generation quality and mitigates repetition in greedy decoding. While effective, the underlying mechanism remains poorly understood, with the extremely low-entropy output distributions suggesting a potential equivalence to simple temperature scaling. In this work, we demonstrate that this phenomenon is fundamentally distinct from distribution sharpening; entropy-matched control experiments re
John Nyland, Ryan Krupp · 2022 · Arthroscopy
Abstract Severe anterior shoulder instability with glenoid bone loss can be very difficult to treat. A recent cadaveric, biomechanical, time‐zero study compared the stability of Bankart repair with long head of the biceps brachi transfer…
M. Telwak, P. Voglewede, M. B. Silver-Thorn · 2014 · Journal of Medical Devices
Recent advances in lower limb prostheses have involved the design of active, powered prosthetic knee and ankle-foot components capable of generating knee and ankle torques similar to that of normal gait. The associated componentry results…
Sabatini, Molinero-Mourelle, Limones et al. · 2026 · The Journal of prosthetic dentistry
Whether the manufacturing protocol (build orientation and sintering schedule) of additively manufactured (AM) zirconia impacts the bond strength compared with subtractively manufactured (SM) zirconia remains unclear. The purpose of this in…
Ping Chen, Zezhou Chen, Xingpeng Zhang et al. · 2026 · arXiv
Current 2D-to-3D conversion methods achieve geometric accuracy but are artistically deficient, failing to replicate the immersive and emotionally resonant experience of professional 3D cinema. This is because geometric reconstruction paradigms mistake deliberate artistic intent, such as strategic zero-plane shifts for pop-out effects and local depth sculpting, for data noise or ambiguity. This paper argues for a new paradigm: Artistic Disparity Synthesis, shifting the goal from physically accurate disparity estimation to artistically coherent disparity synthesis. We propose Art3D, a preliminar
Maria Zenkova · 2025 · ISTORIYA
The author examines the practice of veneration of St. Clement of Rome within the Western and Eastern Christian churches, as well as in Iceland, where his cult was less widespread. Based on the image of St. Clement in Old Norse literature,…
Takuto Yamamoto, Hirosato Akahoshi, Shigeru Kitazawa · 2024 · arXiv
Many models of visual attention have been proposed so far. Traditional bottom-up models, like saliency models, fail to replicate human gaze patterns, and deep gaze prediction models lack biological plausibility due to their reliance on supervised learning. Vision Transformers (ViTs), with their self-attention mechanisms, offer a new approach but often produce dispersed attention patterns if trained with supervised learning. This study explores whether self-supervised DINO (self-DIstillation with NO labels) training enables ViTs to develop attention mechanisms resembling human visual attention.
Joseph Cullen, Gregory Holloway · 2025 · Glottodidactica
This article presents an evaluation of the LSP-TEOC.Pro project. It sets out the evaluation methodology applied, how it was implemented and the key evaluation findings. Given the exploratory nature of the project, the range and complexity…
Chenglong Ma, Ziqi Xu, Yongli Ren et al. · 2025 · arXiv
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited modelling of user personality traits. While simulation frameworks can generate synthetic data to address these gaps, existing methods fail to replicate behavioural diversity, limiting their effectiveness. To overcome these challenges, we propose the Personality-driven User Behaviour Simulator (PUB), an LLM-based simulation framework that integrates the Big Five personality traits to model personalised user behaviour. PU
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh et al. · 2022 · arXiv
Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that policy stabilization can be learned via simple augmentations. In real-world scenarios, demonstrations are often of diverse-quality, and disturbance injection instead learns sub-optimal policies that fail to replicate desired behavior. To address this issue, this paper proposes a novel imitation learning framework that combines both policy robustification and optimal demonstration learning. Specifically, this combinato
Carly Cunningham, Stuart Cantlay, Joseph Horzempa · 2019 · Proceedings of the West Virginia Academy of Science
CARLY CUNNINGHAM, STUART CANTLAY, AND JOSEPH HORZEMPA, Department of Natural Sciences and Mathematics, West Liberty University, West Liberty, WV USA. The ability of VBNC F. tularensis to replicate within THP-1 cells. Francisella…
María Pereda · 2024 · arXiv
The problem of free-riding arises when individuals benefit from a shared resource, service, or public good without contributing proportionately to its provision. This conduct often leads to a collective action problem, as individuals pursue personal gains while relying on the contributions of others. In this study, we present a Bayesian inference model to elucidate the behaviour of participants in a Public Goods Game, a conceptual framework that captures the essence of the free-riding problem. Here, individuals possess information on the distribution of group donations to the public good. Our
Damian Ruck, R. Alexander Bentley, Alberto Acerbi et al. · 2017 · arXiv
Here we test Neutral models against the evolution of English word frequency and vocabulary at the population scale, as recorded in annual word frequencies from three centuries of English language books. Against these data, we test both static and dynamic predictions of two neutral models, including the relation between corpus size and vocabulary size, frequency distributions, and turnover within those frequency distributions. Although a commonly used Neutral model fails to replicate all these emergent properties at once, we find that modified two-stage Neutral model does replicate the static a
Sarika Arora, Mike Schmidt, James Boylan et al. · 2022 · Journal of Consciousness Studies
Previous research suggests anxious individuals demonstrate hypervigilance for threatening stimuli. Recent controversial studies suggested people's bodies (presentiment) and brain (precognition, Bem, 2011; Bem et al., 2015) can 'predict'…
Falco J. Bargagli-Stoffi, Omar Melikechi · 2026 · arXiv
Identifying covariates that modify treatment effects is a central problem in causal inference. Yet existing data-adaptive procedures do not provide finite-sample control over the expected number of false discoveries, risking spurious findings that fail to replicate. We introduce causal stability selection, an algorithm that combines cross-fitted estimation of conditional average treatment effects with integrated path stability selection. The method accommodates arbitrary treatment effect estimators and arbitrary base selectors, and produces a selection set with an explicit, non-asymptotic boun
Francesco Mercadante · 2026 · Pragmatics and Society
Abstract This study proposes a redefinition of the pragmatic foundations of linguistic communication, based on the notion of failed execution as an autonomous analytical category. Drawing on qualitative analysis of a curated illustrative…
· 2018 · The Engineer
The safety-first Subaru XV proves that compact crossovers can handle more than just the school run, writes Chris Pickering
A Cardona Barberán · 2024 · Human Reproduction
Abstract Over the past decade, advancements in next-generation sequencing technology, alongside reduced costs, and the rising demand for infertility medical care, have resulted in the discovery of new genes and genetic variants linked to…
Yali Jiang · 2016 · Chinese Sociological Dialogue
Using a large dataset from the China Educational Panel Survey (CEPS) of 2013–2014 (n=1,593), this paper identifies possible reasons that affect the academic achievement of students in grades 7 and 9 who come from migrant families, and…
Venida Devenida · 2020 · Revista SOBRE
Al igual que las fronteras entre países, las fronteras entre sexos, géneros, razas y etnias están controla- das y administradas por guardianes médicos, legales y tecnológicos. Las identidades fuera de la norma desafían los parámetros…
Zichao Wang, Alexa Siu · 2026 · arXiv
Large language models (LLMs) have shown strong performance on standardized social science instruments, but their value for product discovery remains unclear. We investigate whether interview-informed generative agents can simulate user responses in concept testing scenarios. Using in-depth workflow interviews with knowledge workers, we created personalized agents and compared their evaluations of novel AI concepts against the same participants' responses. Our results show that agents are distribution-calibrated but identity-imprecise: they fail to replicate the specific individual they are gro
Juliana Felipetto Cargnelutti, Adriéli Wendlant, Rudi Weiblen et al. · 2012 · Ciência Rural
The origin of vaccinia viruses (VACV) associated with vesicular disease in cattle and humans in Southeast Brazil remains uncertain, yet the role of wild species in virus transmission has been suggested. This study investigated the…
Kimberly Gray, Susan Hickenbottom · 2016 · Stroke
Background: Readmissions can negatively impact patients, families and health care organizations. Readmission penalties have resulted in focused efforts around discharge planning, patient engagement and transitional care. Purpose: We…
Shintaro Sakai, Jisun An, Migyeong Kang et al. · 2025 · arXiv
Prior clinical psychology research shows that Western individuals with depression tend to report psychological symptoms, while Eastern individuals report somatic ones. We test whether Large Language Models (LLMs), which are increasingly used in mental health, reproduce these cultural patterns by prompting them with Western or Eastern personas. Results show that LLMs largely fail to replicate the patterns when prompted in English, though prompting in major Eastern languages (i.e., Chinese, Japanese, and Hindi) improves alignment in several configurations. Our analysis pinpoints two key reasons