Low Standard Error Research Articles

Intra and inter-tester reliability of a novel device to assess gluteal muscle strength in professional football players

ABSTRACT The aim of this study is to investigate inter-tester and intra-tester reliability of a novel clam test (CLAMT) for the measurement of gluteal muscle strength and to detect possible differences between CLAMT values in football players with and without a history of groin injuries. Twenty male football players participated in the test-retest and sixty-two male professional football players participated in the case–control study. Hip abductor maximal muscle strength was evaluated either using CLAMT or in a supine position with the hip in a neutral pose. For CLAMT, intraclass correlation coefficient (ICC) for inter-tester-intra-day reliability was 0.80 (95% CI: 0.60–0.90), with a standard error of measurement of 34.2 N. The intra-tester-intra-day ICC was 0.92 (95% CI: 0.87–0.95), with a standard error of measurement of 23.6 N. The inter-week ICC was 0.96 (95% CI: 0.92–0.98), with a standard error of measurement of 18.9 N. CLAMT showed lower (but not significant) strength values in football players with a history of groin injuries to non-injured players. CLAMT showed good to excellent levels of reliability, intraday and inter-week, with low standard errors of measurement while it was effective (possible) to identify residual weakness in players with previous groin injuries.

A Comparison of Two Methods for Sampling Bluegill and Redear Sunfish in Small Impoundments

AbstractBluegill Lepomis macrochirus and Redear Sunfish L. microlophus are highly regarded recreational species throughout North America. Management of these species relies on sampling methods that accurately and efficiently describe population characteristics of the target population. Largemouth Bass Micropterus salmoides and crappies Pomoxis spp. are commonly sampled with spring electrofishing and fall fyke netting, respectively. If lepomids can be effectively sampled concurrently with Largemouth Bass or crappies with one of these sampling methods, it would improve sampling efficiency in small impoundments. Our objective was to compare two sampling methods (North American standard fyke netting and electrofishing) for sampling Bluegill and Redear Sunfish populations in small impoundments by comparing size structure, sampling precision (relative standard error), and sampling efficiency (effort needed to collect 125 stock‐length fish). Typically, spring electrofishing caught a wider size range of Bluegill and Redear Sunfish than fyke nets, but proportional size distribution was similar between gears. However, length frequencies differed between gears in all impoundments for Bluegill and in three of five impoundments for Redear Sunfish. Electrofishing typically caught a greater proportion of large lepomids than fyke nets in those cases, though the differences were subtle and management decisions would likely be similar using data from either gear. With the exception of Pawhuska Lake (which had low catch rates leading to poor precision for both species and both gears), catch rates were typically high enough to collect sufficient numbers (125 stock‐length fish) of Bluegill to adequately describe size structure with 5–20 net‐nights for fyke nets or 4–28 transects for electrofishing, but Redear Sunfish sampling would require considerably more sampling effort to produce sufficient amounts of fish in most cases (24–79 net‐nights for fyke nets or 12–57 transects for electrofishing). Spring electrofishing is the more precise (lower relative standard error) of the two sampling methods for collecting lepomids.

Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis.

The objective of this study was to develop a rapid technique to authenticate potato chip frying oils using vibrational spectroscopy signatures in combination with pattern recognition analysis. Potato chip samples (n = 118) were collected from local grocery stores, and the oil was extracted by a hydraulic press and characterized by fatty acid profile determined by gas chromatography equipped with a flame ionization detector (GC-FID). Spectral data was collected by a handheld Raman system (1064 nm) and a miniature near-infrared (NIR) sensor, further being analyzed by SIMCA (Soft Independent Model of Class Analogies) and PLSR (Partial Least Square Regression) to develop classification algorithms and predict the fatty acid profile. Supervised classification by SIMCA predicted the samples with a 100% sensitivity based on the validation data. The PLSR showed a strong correlation (Rval > 0.97) and a low standard error of prediction (SEP = 1.08–3.55%) for palmitic acid, oleic acid, and linoleic acid. 11% of potato chips (n = 13) indicated a single oil in the label with a mislabeling problem. Our data supported that the new generation of portable vibrational spectroscopy devices provided an effective tool for rapid in-situ identification of oil type of potato chips in the market and for surveillance of accurate labeling of the products.

Detection of Orange Essential Oil, Isopropyl Myristate, and Benzyl Alcohol in Lemon Essential Oil by FTIR Spectroscopy Combined with Chemometrics

Essential oils are high-valued natural extracts that are involved in industries such as food, cosmetics, and pharmaceutics. The lemon essential oil (LEO) has high economic importance in the food and beverage industry because of its health-beneficial characteristics and desired flavor properties. LEO, similar to other natural extracts, is prone to being adulterated through economic motivations. Adulteration causes unfair competition between vendors, disruptions in national economies, and crucial risks for consumers worldwide. There is a need for cost-effective, rapid, reliable, robust, and eco-friendly analytical techniques to detect adulterants in essential oils. The current research developed chemometric models for the quantification of three adulterants (orange essential oil, benzyl alcohol, and isopropyl myristate) in cold-pressed LEOs by using hierarchical cluster analysis (HCA), principal component regression (PCR), and partial least squares regression (PLSR) based on FTIR spectra. The cold-pressed LEO was successfully distinguished from adulterants by robust HCA. PLSR and PCR showed high accuracy with high R2 values (0.99–1) and low standard error of cross-validation (SECV) values (0.58 and 5.21) for cross-validation results of the raw, first derivative, and second derivative FTIR spectra. The findings showed that FTIR spectroscopy combined with multivariate analyses has a considerable capability to detect and quantify adulterants in lemon essential oil.

Zinc sorption characteristics and release kinetics from soils with long-term zinc and phosphorus application

We investigated the effect of long-term phosphorus (P) and zinc (Zn) application in three soils (Soil-I, Soil-II and Soil-III) with differential P content on Zn sorption and release kinetics to understand the underlying mechanism controlling Zn sorption-desorption reactions. Zn sorption was highest in soil-III (54.4-96.4% of added Zn), and was the lowest in soil-I (44.1-94.3% of added Zn) in no-P applied soils. In soil-III, 60.6-97.6% of applied Zn gets sorbed on soil colloidal complex with P application at 100 mg P kg-1, which was 1.2-11.4% higher at differential levels of Zn application. Results showed that Langmuir sorption maxima (b) and Freundlich’s adsorptive capacity (Y) were lowest for soil-I without P application and was the highest in soil-III with P application. These results suggests that soil-III with P application at 100 mg P kg-1 with highest Zn sorption had the lowest Zn release capacity. Further, these results showed that Elovich equation best described the kinetics of Zn release from three soils with highest value of coefficient of determination (R2=0.93-0.98*, p<0.05) and lowest standard error (S.E.= 0.172-0.256).

Accounting as a Source of Financial Information on Insolvency in Liquidation Proceedings

Purpose: The purpose of this paper was to indicate which information originating in accounting is important for ensuring the information transparency of bankruptcy proceedings. Methodology: Based on the literature review and the analysis of international legal acts, we determined priority types of information from accounting used in bankruptcy court proceedings. These types included information on the costs of insolvency proceedings, the assumption in the financial statements that there was no going concern, and the book value of assets. A survey was then conducted on a group of specialists in the field of bankruptcy. Findings: Respondents indicated information on the costs of bankruptcy proceedings as the most important. However, other accounting information under study also received a high assessment from insolvency specialists. The obtained assessments of individual quality features are characterized by high reliability confirmed by low standard errors. Research implications: Accounting evolves as a response to the information needs of accounting information users. Therefore, knowing these needs may contribute to changes in the requirements regarding the scope of information from accounting that is used in bankruptcy proceedings. Originality/value: In-depth knowledge of various aspects of the economic side of the bankruptcy phenomenon is part of the current trend in minimizing the negative effects of bankruptcy.

In Situ Monitoring of Sugar Content in Breakfast Cereals Using a Novel FT-NIR Spectrometer

This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (Rpre ≥ 0.93) and low standard error of prediction (SEP ≤ 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.

Using ridge regression to analysis the meteorological data in sulaimani

Linear regression is one of the frequently used statistical methods that have applications in all field of daily life. In a statistical perspective, the regression analysis is used for studying the relationship between a dependent variable and a set of independent variables. The ridge regression is the most widely model in solving the multicolinearity problem, and it''''s an alternative to OLS.Multicollinearity is the most common problem in multiple regression models in which there exists a perfect relationship between two explanatory variables or more in the model. In this study, ridge regression model was used to estimate linear regression model. This result was compared with result obtained using ordinary least squares model in order to find the best regression model. We have used meteorological data in this study. The results showed that the ridge regression method can be used to resolve the multicollinearity problem, without deleting the independent correlated variables of the model and able to estimate parameters with lower standard error values.

Estimating population density of the medicinal leech Hirudo orientalis Utevsky and Trontelj, 2005 (Clitellata: Hirudinida) using Visible Implant Elastomer tags

ABSTRACT Medicinal leeches have lately come into the spotlight as useful and effective invertebrates in the treatment of some diseases and surgical procedures. Populations of the Iranian medicinal leech, Hirudo orientalis, are under considerable threat due to over-exploitation. To find a viable technique for estimating leech population size in their natural habitat, a tagging method using fluorescent dye was investigated. In the present study, to test the method’s efficiency, numbers of medicinal leeches were estimated in the artificial leech ponds of the University of Tehran, by the capture–mark–recapture method, using the Lincoln index. Leeches were caught from the ponds, marked using commercially available visible implant elastomer (VIE) tags, released and later recaptured. The technique proved successful, and low standard error values (7.07–11.85) in this study showed that the VIE tagging method could be applied as a useful tool for evaluating medicinal leech population size. This is the first use of this method to estimate medicinal leech population size.

Methodology for efficient parametrisation of electrochemical PEMFC model for virtual observers: Model based optimal design of experiments supported by parameter sensitivity analysis

Determination of the optimal design of experiments that enables efficient parametrisation of fuel cell (FC) model with a minimum parametrisation data-set is one of the key prerequisites for minimizing costs and effort of the parametrisation procedure. To efficiently tackle this challenge, the paper present an innovative methodology based on the electrochemical FC model, parameter sensitivity analysis and application of D-optimal design plan. Relying on this consistent methodological basis the paper answers fundamental questions: a) on a minimum required data-set to optimally parametrise the FC model and b) on the impact of reduced space of operational points on identifiability of individual calibration parameters. Results reveal that application of D-optimal DoE enables enhancement of calibration parameters information resulting in up to order of magnitude lower relative standard errors on smaller data-sets. In addition, it was shown that increased information and thus identifiability, inherently leads to improved robustness of the FC electrochemical model.

Facial Fabrication and Characterization of Novel Ag/AgCl Chloride Ion Sensor Based on Gel-Type Electrolyte.

In this study, a novel chloride ion (Cl−) sensor based on Ag wire coated with an AgCl layer was fabricated using a gel-type internal electrolyte and a diatomite ceramic membrane, which played an important role in preventing electrolyte leakage from the ion-selective electrode. The sensing performance, including reversibility, response, recovery time, low detection limit, and the long-term stability, was systemically investigated in electrolytes with different Cl− contents. The as-fabricated Cl− sensor could detect Cl− from 1 to 500 mM KCl solution with good linearity. The best response and recovery time obtained for the optimized sensor were 0.5 and 0.1 s, respectively, for 10 mM KCl solution. An exposure period of over 60 days was used to evaluate the stability of the Cl− sensor in KCl solution. A relative error of 2% was observed between the initial and final response potentials. Further, a wireless sensing system based on Arduino was also investigated to measure the response potential of Cl− in an electrolyte. The sensor exhibited high reliability with a low standard error of measurement. This type of sensor is crucial for fabricating wireless Cl− sensors for applications in reinforced concrete structures along with favorable performances.

Effects of feather processing methods on quantity of extracted corticosterone in broiler chickens.

Corticosterone is known as a biological stress index in many species including birds. Feather corticosterone concentration (FCC) has increasingly been used as a measure for chronic stress status in broiler chickens. As sample preparation is the first step of analytical process, different techniques of feather matrix disruption need to be validated for obtaining better result in analysing corticosterone extraction. The current study was a validation of pulverizing the feather by bead beater (BB) and surgical scissors (SS) processing prior to corticosterone extraction in feather of broiler chickens. The type of feather processing prior to the hormone extraction may alter the final output. Thereby, finding a standard method according to laboratory facilities is pivotal. This study carried out to determine the effects of feather pulverization methods on the extraction amount of corticosterone in broiler chickens. Feathers were sampled from four weeks old Ross 308 broiler chickens (n = 12 birds). All broiler chickens were kept under the same environmental condition and had access to feed and water. Feather samples were assigned to one of the following processing methods 1) using a BB for pulverizing and 2) using a SS for chopping into tiny pieces. Each sample was duplicated into two wells during enzyme immunoassay (EIA) analysis to improve the accuracy of the obtained data. The results showed lower standard errors and constant output of FCC by using the BB method compared with the SS method. Overall comparison of FCC showed a significantly higher (p < 0.001) amount of the FCC in the BB compared with the SS. Overall, using the BB method is recommended over the SS method for feather processing due to the ability to homogenize a large number of samples simultaneously, ease of use and greater extraction of feather corticosterone.

Human health risk estimation and predictive modeling of halogenated disinfection by- products (chloroform) in swimming pool waters: a case study of Dhanbad, Jharkhand, India.

Disinfection is an important process to make the water free from harmful pathogenic substances, but sometimes it results in the formation of harmful by-products. Development of predictive models is required to define the concentration of THMs in pool water. Majority of studies reported inhalation to be the most significant THMs exposure route which is more likely to be dependent upon the concentration of THMs in pool water and in air. THMs concentration in the analyzed pool water samples and in air was found to be 197.18 ± 16.31μgL-1 and 0.033μgm3-1, respectively. Statistical parameters such as high correlation coefficients, high R2 values, low standard error, and low mean square error of prediction indicated the validity of MLR based linear model over non-linear model. Therefore, linear model can be most suitably used to pre-assess and predict the THMs levels in swimming pool water. Risk estimation studies was conducted by using the united states environmental protection agency (USEPA) Swimmer Exposure Assessment Model (SWIMODEL). The lifetime time cancer risk values related to chloroform exceeded 10-6 for both the sub-population. Inhalation exposure leads to maximum risk and contributed up to 99% to total cancer risk. Risk due to other exposure pathways like accidental ingestion and skin contact was found to be negligible and insignificant. Monte Carlo simulation results revealed that the simulated THMs risk values for the studied exposure pathways lies within ±3.1% of the average risk values obtained using SWIMODEL. Hence, the risk estimates obtained using SWIMODEL seemed to be appropriate in determining the potential risk exposure of THMs on human health. Variation in input parameters like body weight (BW) and skin surface area (SA) leads to difference in risk estimates for the studied population. Non cancer risk was found to be insignificant as represented by low hazard quotient (HQ < 1) values. Through monitoring and regulations on control of THMs in swimming pool water is required to minimize the risk associated.

Minimal-Invasive Analytical Method and Data Fusion: an Alternative for Determination of Cu, K, Sr, and Zn in Cocoa Beans

In the present study, a minimal-invasive analytical method for determination of Cu, K, Sr, and Zn in cocoa beans was performed using energy X-ray fluorescence (EDXRF) and laser-induced breakdown spectroscopy (LIBS) combined with multivariate calibration. Partial least squares (PLS) chemometric technique was applied to modeling the data, and inductively coupled plasma optical emission spectrometry (ICP OES) technique was the reference method for the chemical elements concentration levels, after microwave acid mineralization. The figures of merit estimated for Cu, Sr, Zn, and K showed good performance, with acceptable trueness values (85–120%). Data fusion strategy between EDXRF and LIBS data was used to enhance the predictive capability of K. In addition, lower standard error of cross-validation (SECV) (872 mg kg−1) was obtained, showing better performance than those obtained by individual data.

Trade-off assessments between reading cost and accuracy measures for digital camera monitoring of recreational boating effort

Digital camera monitoring is increasingly being used to monitor recreational fisheries. The manual interpretation of video imagery can be costly and time consuming. In an a posteriori analysis, we investigated trade-offs between the reading cost and accuracy measures of estimates of boat retrievals obtained at various sampling proportions for low, moderate and high traffic boat ramps in Western Australia. Simple random sampling, systematic sampling and stratified sampling designs with proportional and weighted allocation were evaluated to assess trade-offs in terms of bias, accuracy, precision, coverage rate and cost in estimating the annual total number of powerboat retrievals in 10,000 jackknife resampling draws. The relative standard error (RSE ± standard deviations) obtained by the sampling designs for sampling proportions from 0.4 onwards were below a 20 % threshold for three of the sampling designs across the three boat ramps. Coverage rates of over 90 % were observed for the confidence intervals for the estimated annual number of powerboat retrievals, with low relative standard errors (RSE < 20 %). Interpreting 40 % of camera footage within a year provided the minimum level to obtain sufficient accuracy measures for all sampling designs considered. The stratified random sampling design with weighted allocation consistently resulted in the smallest variance for estimates of annual powerboat retrievals across the various sampled proportions. These findings have the potential to considerably reduce the cost of manual data interpretation, since operating cost increased linearly with increasing sampling proportion.

Maximising the ability of stimulus-frequency otoacoustic emissions to predict hearing status and thresholds using machine-learning models

Objective This study aimed to maximise the ability of stimulus-frequency otoacoustic emissions (SFOAEs) to predict hearing status and thresholds based on machine-learning models. Design SFOAE data and audiometric thresholds were collected at octave frequencies from 0.5 to 8 kHz. Support vector machine, k-nearest neighbour, back propagation neural network, decision tree, and random forest algorithms were used to build classification models for status identification and to develop regression models for threshold prediction. Study sample About 230 ears with normal hearing and 737 ears with sensorineural hearing loss. Results All classification models yielded areas under the receiver operating characteristic curve of 0.926–0.994 at 0.5–8 kHz, superior to the previous SFOAE study. The regression models produced lower standard errors (8.1–12.2 dB, mean absolute errors: 5.53–8.97 dB) as compared to those for distortion-product and transient-evoked otoacoustic emissions previously reported (8.6–19.2 dB). Conclusions SFOAEs using machine-learning approaches offer promising tools for the prediction of hearing capabilities, at least at 0.5–4 kHz. Future research may focus on further improvements in accuracy and reductions in test time to improve clinical utility.

Estimation of Phenotypic and Genetic Trends in Economic Traits of Murrah Buffaloes

Background: Notable advancements in the selection indices have led to the development of exceptional breeding strategies, which have eventually led to the selection of elite dairy animals. The comparison of trends is facilitated by their estimation which permit the assessment of progress in a particular trait. The purpose of this study was to investigate genetic trends in economic traits of Murrah buffaloes, which is predominantly present as a high milk producer using the regression method. The study was designed to estimate the phenotypic, genetic and environmental trends, which represent the changes accrued over time and hence, determines the efficiency of breeding programme. Methods: The present study was conducted on 659 Murrah buffaloes, sired by 188 bulls, over a period of 27 years (1991-2017). The economic traits studied were first lactation milk yield (FLMY), first lactation length (FLL), age at first calving (AFC), first calving interval (FCI), first service period (FSP) and first dry period (FDP). The phenotypic trends were obtained as regression of population performance on year and the genetic trends were obtained by Smith method I and II. Result: The results revealed that the phenotypic trends obtained were significant and desirable for all the traits except for first lactation length, which was non-significant. The standard errors obtained by Smith method-I were higher than those obtained from method-II. As the Smith method-II avoids year to year fluctuations in the environment and those of contemporaries, it gives more realistic estimate as compared to the first method with lower standard error.

Sustainable Governance of Coastal Areas and Tourism Impact on Waste Production: Panel Analysis of Croatian Municipalities

The research problem this paper is concerned with is the effect of tourism on solid waste generation in Croatia’s coastal area. We are aware of the fact that this has not been thoroughly addressed, especially considering tourism’s share in the Croatian economy and the pressure it generates on sustainable coastal management. This is of particular importance considering the governing complexity of coastal areas. Thus, we ask a simple question regarding the role of the tourism industry in the solid waste generation in the Croatian coastal area: Do tourists generate relatively more solid waste than the domestic population? The falsifiable hypothesis is stated in terms of the difference in the production of communal waste between domestic population and tourists, taking into account local idiosyncratic factors, when such a difference is recoverable through statistical analysis of measurable tourist presence in panel data. The first hypothesis is thus: The amount of solid waste produced by local residents in Croatian coastal municipalities diverges significantly in statistical terms from the amount of solid waste produced by tourists. The second hypothesis is: The amount of waste-streams is influenced by local idiosyncrasies of coastal settlements, their economic structure, per capita GDP and/or cultural background of local people. Our dataset is a panel of 160 municipalities in the Croatian coastal area spanned across a time period of 12 months during 2019, giving us a total of 1920 panel observations. We performed a Panel Estimated Generalized Least Squares cross-section fixed effects analysis with Panel Corrected Standard Errors on domestic population and tourist overnight stays and their solid waste generation. We used the above mentioned method to achieve better results with higher significance, and lower Standard Errors than comparable methods. We complemented the analysis with a dynamic Panel Generalized Method of Moments First Differences test. Results show a relatively larger relative impact of tourist overnight stays on municipal solid waste generation than what is to be expected from the locals only. Our different methods of analysis ended with non-contradicting results. The number of tourist overnight stays in some municipalities shadows the overnight stays of the local population as an indicator of solid waste generation, exacerbating the problem of sustainability of waste disposal. We conclude that the relative waste disposal impact of the tourists is at least 22% greater and possibly up to 55% greater than the one of local inhabitants, contradicting some other research. We also found evidence of possible Environmental Kuznets Curve behavior.

Derivation and validation of a simple anthropometric equation to predict fat-free mass in patients with chronic hepatitis C

Loss of skeletal muscle mass is very common in chronic liver diseases and affects 30.0-70.0% of the patients with cirrhosis. Given the relevance of muscle wasting in hepatic diseases, a practical screening tool for earlier detection of skeletal muscle mass loss is of utmost significance. To develop and validate a simple anthropometric prediction equation for fat-free mass estimation by using Bioelectrical Impedance Analysis (BIA) as a reference method in patients with chronic hepatitis C (CHC). We prospectively, included 209 CHC patients, randomly allocated into two groups, 158 patients in a development model (derivation sample) and 51 patients in a validation group (validation sample). Predictive equations were developed using backward stepwise multiple regression and the most adequate and simplest derived predictive equation was further explored for agreement and bias in the validation sample. The accuracy of the predictive equation was evaluated using the coefficient of determination (R2). The predictive equation with an optimal R2 was Fat-Free Mass (Kg)=Sex×0.17+Height (m)×16.83+Weight (Kg)×0.62+Waist Circumference (cm)×(-0.15)+Weight (Kg)×Sex×(-0.30)+Sex×Waist Circumference (cm)×0.14-6.23; where sex=1 for female and 0 for male. R2=0.93, standard error of the estimate=2.6Kg and coefficient of variation=20.0%, p<0.001. Our developed and cross-validated anthropometric prediction equation for fat-free mass estimation by using BIA attained a high coefficient of determination, a low standard error of the estimate, and lowermost coefficient of variation. This study indicates that predictive equations may be reliable and useful alternative methods for clinical evaluation of fat-free mass in patients with CHC.

ContamLD: estimation of ancient nuclear DNA contamination using breakdown of linkage disequilibrium

We report a method called ContamLD for estimating autosomal ancient DNA (aDNA) contamination by measuring the breakdown of linkage disequilibrium in a sequenced individual due to the introduction of contaminant DNA. ContamLD leverages the idea that contaminants should have haplotypes uncorrelated to those of the studied individual. Using simulated data, we confirm that ContamLD accurately infers contamination rates with low standard errors: for example, less than 1.5% standard error in cases with less than 10% contamination and 500,000 sequences covering SNPs. This method is optimized for application to aDNA, taking advantage of characteristic aDNA damage patterns to provide calibrated contamination estimates, and is available at https://github.com/nathan-nakatsuka/ContamLD.