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Failure-mode index

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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.

130 results in Methods Dead-End · page 5 of 5

Methods Dead-EndMedicine

Long-term Assessment of Oral Health-Related Quality of Life Following Surgical Removal of Mandibular Third Molar with Advanced Platelet-rich Fibrin: a Single-blinded Randomized Controlled Trial.

Starch-Jensen, Giordano, Alsadi et al. · 2026 · Journal of oral & maxillofacial research

The aim of this single-blinded randomized controlled trial was to test the hypothesis of no difference in long-term oral health-related quality of life following surgical removal of an impacted mandibular third molar with advanced…

View details →DOI: 10.5037/jomr.2026.17101
Methods Dead-EndOpen accessComputer Science

LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation

Aytaç Sekmen, Fatih Emre Gunes, Furkan Horoz et al. · 2026 · arXiv

Monocular Depth Estimation (MDE) is crucial for autonomous lunar rover navigation using electro-optical cameras. However, deploying terrestrial MDE networks to the Moon brings a severe domain gap due to harsh shadows, textureless regolith, and zero atmospheric scattering. Existing evaluations rely on analogs that fail to replicate these conditions and lack actual metric ground truth. To address this, we present LuMon, a comprehensive benchmarking framework to evaluate MDE methods for lunar exploration. We introduce novel datasets featuring high-quality stereo ground truth depth from the real C

Methods Dead-EndOpen accessMathematics

The Constrained Maximum Likelihood Estimation For Parameters Arising From Partially Identified Models

Hao Luo, Alexandre Bouchard-Côté, Gabriela Cohen Freue et al. · 2016 · arXiv

We extend the constrained maximum likelihood estimation theory for parameters of a completely identified model, proposed by Aitchison and Silvey (1958), to parameters arising from a partially identified model. With a partially identified model, some parameters of the model may only be identified through constraints imposed by additional assumptions. We show that, under certain conditions, the constrained maximum likelihood estimator exists and locally maximize the likelihood function subject to constraints. We then study the asymptotic distribution of the estimator and propose a numerical algo

Methods Dead-EndOpen accessComputer Science

Another Facet of LIG Parsing

Pierre Boullier · 1996 · arXiv

In this paper we present a new parsing algorithm for linear indexed grammars (LIGs) in the same spirit as the one described in (Vijay-Shanker and Weir, 1993) for tree adjoining grammars. For a LIG $L$ and an input string $x$ of length $n$, we build a non ambiguous context-free grammar whose sentences are all (and exclusively) valid derivation sequences in $L$ which lead to $x$. We show that this grammar can be built in ${\cal O}(n^6)$ time and that individual parses can be extracted in linear time with the size of the extracted parse tree. Though this ${\cal O}(n^6)$ upper bound does not impro

Methods Dead-End

The Developing "The Most Significant Change Technique (MSC) Model" in Physical Education Learning in High Schools

Kristi Agust, Fekie Adila, Imam Rahmatullah et al. · 2023 · Journal Physical Health Recreation

Tujuan dari penelitian ini adalah mengembangkan model evaluasi teknik The Most Significant Change Technique paling progresif dalam pembelajaran PJOK di Sekolah Menengah Atas. Metodologi yang digunakan adalah Researeh & Development dengan…

View details →DOI: 10.55081/jphr.v3i2.847
Methods Dead-EndOpen accessComputer Science

Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting

Konstantin Hopf, Hannah Hartstang, Thorsten Staake · 2023 · arXiv

Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review articles exist, there is surprisingly little quantitative analysis of the literature, most notably none that identifies the impact of factors on forecasting performance across the entirety of empirical studies. In this article, we therefor

Methods Dead-EndOpen accessMedicine (General)

Evaluating the psychometric properties of the attitudes towards depression and its treatments scale in an Australian sample

Di Benedetto M, Greenwood KM, Isaac F · 2012 · Patient Preference and Adherence

Fadia Isaac1, Kenneth Mark Greenwood2, Mirella Di Benedetto31Cairnmillar Institute School of Psychology Counselling and Psychotherapy, Camberwell, Victoria, Australia; 2School of Psychology and Social Science Faculty of Computing, Health and Science, Edith Cowan University, Joondalup, Western Australia, Australia; 3School of Health Sciences, Royal Melbourne Institute of Technology University, Bundoora, Victoria, AustraliaBackground: Individuals’ attitudes towards depression and its treatments may influence their likelihood of seeking professional help and adherence to treatment when depr

Methods Dead-EndOpen accessFinance

Predictive Power of ESG Factors for DAX ESG 50 Index Forecasting Using Multivariate LSTM

Manuel Rosinus, Jan Lansky · 2025 · International Journal of Financial Studies

As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return forecasts for German DAX-listed firms. We employ a multivariate long short-term memory (LSTM) network, a machine learning model ideal for time series data, to test this hypothesis over two periods: an 8-year analysis with a full set of ESG scores and a 16-year analysis with a single disclosure score. The evalu

View details →DOI: 10.3390/ijfs13030167