Subjective Quality Evaluation Research Articles

The Impact of Lunch Timing on Nap Quality

Purpose: Previous research has established that food intake is a biological regulator of the human sleep–wake cycle. As such, the timing of eating relative to sleep may influence the quality of sleep, including daytime naps. Here, we examine whether the timing of lunch (1 h vs. 2 h interval between lunch and a napping opportunity) impacts the quality of an afternoon nap. Methods: Using a randomized within-subject design over two separate experimental sessions (7 days apart), participants (n = 40, mean age = 25.8 years) consumed lunch 1 h and 2 h prior to an afternoon nap opportunity. Polysomnography and subjective self-reports were used to assess sleep architecture, sleepiness levels, and nap quality. Results: Results revealed no significant differences in subjective ratings of sleep quality and sleepiness, or in sleep architecture (total sleep time, sleep efficiency, sleep onset latency, sleep stages) between the 1 h and 2-h lunch conditions. Conclusions: All sleep measures were similar when napping followed eating by either 1 h or 2 h, suggesting that eating closer to nap onset may not negatively impact sleep architecture and quality. Future research should continue to identify conditions that improve nap quality, given the well-documented benefits of naps to reduce sleep pressure and improve human performance.

A noise-reduction algorithm for raw 3D point cloud data of asphalt pavement surface texture

High-precision 3D point cloud data have various analyses and application use cases. This study aimed to achieve a more precise noise reduction of the raw 3D point cloud data of asphalt pavements obtained using 3D laser scanning. Hence, a noise-reduction algorithm integrating improved Gaussian filtering and coefficient of variation was developed. A portable laser scanner was used to collect raw, high-precision 3D point cloud data of surface textures from pavement slab samples prepared with three different types of asphalt mixtures: AC-13, SMA-13, and OGFC-13, as well as asphalt from the test sections of the Yakang Expressway. An improved Gaussian filtering and Gaussian filtering that extracts noise using the coefficient of variation were used to filter out the obvious outlier noise and small-scale burr noise, respectively. Finally, the filtering effect of the proposed algorithm, Gaussian filtering, median filtering, and mean filtering on raw 3D point cloud data of pavement textures was evaluated through subjective visual quality and objective index evaluations. The results showed that the proposed algorithm filters out noise while preserving the micro-texture structure information, outperforming Gaussian filtering, median filtering, and mean filtering.

Research on a Multidimensional Digital Printing Image Quality Evaluation Method Based on MLP Neural Network Regression

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy.

Research on a Multidimensional Digital Printing Image Quality Evaluation Method Based on MLP Neural Network Regression

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy.

Analyzing the Quality of Distorted Images by the Normalized Mutual Information Measure

This research explores how different types of distorting algorithms impact the Full-Reference image quality assessment, particularly when subjective quality evaluations are incorporated. We draw upon the TID2013 database, which contains 3000 images distorted by 24 distinct algorithms, in conjunction with Mean Opinion Scores (MOS) for quality ratings. We compare the results of Normalized Mutual Information (NMI) for image quality score with W2, based on Weibull distribution, the common PSNR similarity measure and MOS. We advocate for integrating of NMI into the repertoire of image quality assessment metrics.

Investigation in image quality and immediate patient safety using pre-dual-flow injection for low-contrast dose spectral pulmonary artery CT angiography

PurposeThe patient safety of iodine contrast-enhanced pulmonary artery CT angiography (CTPA) is widely concerned. This study aimed to investigate the image quality and immediate patient safety of spectral CTPA using a lower-contrast dose pre-dual-flow injection method. MethodsThis retrospective study included 120 patients with suspected pulmonary embolisms who received spectral CTPA between February and December 2022. Patients were divided into normal contrast injection (Group A, n=60) and pre-dual-flow group (Group B, n=60). CT values of pulmonary arteries (PAs) at different levels, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), arteriovenous separation performance, and beam hardening artifact (BHA) index of two sets of images were measured or calculated. The subjective image quality and immediate patient safety were also scored using the three-point method. ResultsGroup B had a contrast dose reduction by 42.5 % (60 vs. 34.5 mL). Radiation exposure dose was not statistically different between the two groups (P>0.05). CT values of different-level PAs on group B images were higher than those on group A images (P<0.05). Group B images had higher SNR and CNR, better arteriovenous separation between PA trunk and pulmonary vein, and lower BHA index on soft tissue and PA (all P<0.05). For subjective evaluation of image quality, group B had a better score in beam hardening artifact (P<0.05). For immediate patient safety, the score in comfortability was statistically higher in group B, with P<0.05. ConclusionsComparing with the normal injection method, pre-dual-flow spectral CTPA with a lower contrast dose injected results in better image quality and shows potential in patient-safety promotion.

Long COVID-19 Enigma: Unmasking the Role of Distinctive Personality Profiles as Risk Factors.

Background: The COVID-19 (Coronavirus disease 2019) pandemic has prompted extensive research into lingering effects, especially in 'Long COVID' patients. Despite exploration, contributing factors remain elusive; Objective: This study explores the potential link between distinctive personality profiles, particularly type D personality, and an increased risk of Long COVID; Methods: A retrospective cross-sectional study at Tel-Aviv Sourasky Medical Center's Post-COVID clinic analyzed data from 373 Long COVID patients through comprehensive questionnaires covering Long COVID syndrome, Fibromyalgia criteria, personality assessments, social support, and subjective evaluations of cognitive decline, health and life quality. In total, 116 out of 373 patients completed the questionnaire, yielding a 31% participation rate; Results: Cluster analysis revealed two groups, with Cluster 1 (N = 58) exhibiting Type D personality traits while Cluster 2 (N = 56) not meeting criteria for Type D personality. In comparison to Cluster 2, Cluster 1 patients reported heightened anxiety, depression, reduced social support, increased pain symptoms, manifestations of fibromyalgia, cognitive decline, and poor sleep quality, contributing to a diminished quality-of-life perception; Conclusions: findings highlight diverse personality profiles among Long COVID patients, emphasizing the need for tailored care. This approach shows potential for improving Long COVID patient care, aligning with the evolving personalized medicine paradigm.

Deep neural network based distortion parameter estimation for blind quality measurement of stereoscopic images

Stereoscopic/3D image quality measurement (SIQM) has emerged as an active and important research branch in image processing/computer vision field. Existing methods for blind/no-reference SIQM often train machine-learning models on degraded stereoscopic images for which human subjective quality ratings have been obtained, and they are thus constrained by the fact that only a limited number of 3D image quality datasets currently exist. Although methods have been proposed to overcome this restriction by predicting distortion parameters rather than quality scores, the approach is still limited to the time-consuming, hand-crafted features extracted to train the corresponding classification/regression models as well as the rather complicated binocular fusion/rivalry models used to predict the cyclopean view. In this paper, we explore the use of deep learning to predict distortion parameters, giving rise to a more efficient opinion-unaware SIQM technique. Specifically, a deep fusion-and-excitation network which takes into account the multiple-distortion interactions is proposed to perform distortion parameter estimation, thus avoiding hand-crafted features by using convolution layers while simultaneously accelerating the algorithm by using the GPU. Moreover, we measure distortion parameter values of the cyclopean view by using support vector regression models which are trained on the data obtained from a newly-designed subjective test. In this way, the potential errors in computing the disparity map and cyclopean view can be prevented, leading to a more rapid and precise 3D-vision distortion parameter estimation. Experimental results tested on various 3D image quality datasets demonstrate that our proposed method, in most cases, offers improved predictive performance over existing state-of-the-art methods.

WELL-BEING AMONG SENIOR HIGH SCHOOL LEARNERS: A LITERATURE REVIEW

This literature review synthesizes a wide range of scholarly articles and official documents to explore the multifaceted concept of well-being, particularly in the context of education and mental health. Drawing upon theoretical frameworks such as Maslow's hierarchy of needs, ecological systems theory, and positive psychology, the review provides a comprehensive examination of the various dimensions that contribute to individual well-being. Key themes include the importance of addressing physical, social, emotional, and spiritual aspects of well-being within educational settings, as well as the role of holistic wellness programs in promoting overall health and happiness. The review highlights the significance of subjective well-being as a subjective evaluation of an individual's quality of life, emphasizing the need for comprehensive measurement tools that capture both hedonic and eudaimonic aspects of well-being. Furthermore, the review discusses the relationship between well-being and academic achievement, underscoring the reciprocal nature of this association. It also explores the impact of social support networks, emotional intelligence, and coping strategies on well-being outcomes, emphasizing the importance of fostering open communication channels and providing access to support services for students. Moreover, the review addresses emerging challenges and opportunities in the field, such as the growing recognition of mental health literacy as a public health goal and the increasing use of technology, including social media and virtual reality, to assess and enhance well-being. Finally, it discusses relevant policy initiatives, including mental health legislation and educational reforms aimed at promoting student well-being and resilience. Overall, this literature review provides valuable insights into the complex interplay between well-being, education, and mental health, offering implications for research, practice, and policy development in these critical areas.

Subjective and objective quality evaluation of UGC video after encoding and decoding

The development of the Internet and new media technology has given rise to various forms of media to meet users’ usage needs and expectations. User-Generated-Content (UGC) videos have become a way for users to maximize their participation and express themselves. A large number of UGC videos are uploaded every day on platforms such as Tiktok, Kwai, and Station B. However, the quality of UGC videos is also mixed, with videos with poor image quality providing viewers with a lower user experience. Media service providers need to compress videos uploaded by users to reduce transmission bit rates. How to evaluate the quality of compressed UGC videos is crucial for selecting an appropriate bit rate. In this paper, we have established a UGC database called UGC-New. Concretely, 405 UGC source videos are collected from major video platforms and we encoded the original video using H.264 and H.265 to obtain 2430 distorted videos, resulting in a total of 2835 videos in the dataset. We conducted a subjective human study on this database and obtained 56700 human quality ratings recorded by 100 subjects. At the same time, we also tested a series of VQA models on the dataset for objective analysis and evaluation, including a new one, called New-VQA.

The feasibility of half-dose contrast-enhanced scanning of brain tumours at 5.0 T: a preliminary study

PurposeThis study investigated and compared the effects of Gd enhancement on brain tumours with a half-dose of contrast medium at 5.0 T and with a full dose at 3.0 T.MethodsTwelve subjects diagnosed with brain tumours were included in this study and underwent MRI after contrast agent injection at 3.0 T (full dose) or 5.0 T (half dose) with a 3D T1-weighted gradient echo sequence. The postcontrast images were compared by two independent neuroradiologists in terms of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and subjective image quality score on a ten-point Likert scale. Quantitative indices and subjective quality ratings were compared with paired Student's t tests, and interreader agreement was assessed with the intraclass correlation coefficient (ICC).ResultsA total of 16 enhanced tumour lesions were detected. The SNR was significantly greater at 5.0 T than at 3.0 T in grey matter, white matter and enhanced lesions (p < 0.001). The CNR was also significantly greater at 5.0 T than at 3.0 T for grey matter/tumour lesions, white matter/tumour lesions, and grey matter/white matter (p < 0.001). Subjective evaluation revealed that the internal structure and outline of the tumour lesions were more clearly displayed with a half-dose at 5.0 T (Likert scale 8.1 ± 0.3 at 3.0 T, 8.9 ± 0.3 at 5.0 T, p < 0.001), and the effects of enhancement in the lesions were comparable to those with a full dose at 3.0 T (7.8 ± 0.3 at 3.0 T, 8.7 ± 0.4 at 5.0 T, p < 0.001). All subjective scores were good to excellent at both 5.0 T and 3.0 T.ConclusionBoth quantitative and subjective evaluation parameters suggested that half-dose enhanced scanning via 5.0 T MRI might be feasible for meeting clinical diagnostic requirements, as the image quality remains optimal. Enhanced scanning at 5.0 T with a half-dose of contrast agents might benefit patients with conditions that require less intravenous contrast agent, such as renal dysfunction.

Impact of perceptual-learning strategies and background noise on disordered speech intelligibility

Objective: There is a plethora of research showing reduced speech intelligibility for a variety of voice disorders (i.e., dysphonia, alaryngeal). Therapeutic approaches to improve intelligibility typically involve targeting the speaker (e.g., clear speech, reduced rate) with minimal attention to the listener. Therefore, there were three purposes of this study: 1) to determine the impact of background noise on the speech intelligibility of disordered speakers; 2) to determine the impact of providing listeners with perceptual-learning strategies on the speech intelligibility of speakers with a voice disorder; and 3) to determine if subjective ratings of voice quality can predict speech intelligibility. Methods: Sentences were recorded from 12 speakers (2 typical, 3 alaryngeal, 7 dysphonic). Sentences were divided into one of three groups of signal-to-noise ratios (SNR: quiet, +5 dB SNR, and 0 dB SNR) and individually presented to 129 healthy listeners divided into one of three groups (i.e., control, acknowledgment of disorder, cognitive-perceptual strategies). Orthographic transcription was used to assess speech intelligibility. In addition, three expert listeners provided subjective voice quality ratings of all speakers. Results: Listeners had significantly more intelligibility errors with increased background noise (p &lt;.001) and providing strategies to listeners did not result in a statistical improvement level F(6, 486) = 1.53, p = .17, η2p = 0.02. Regression analysis showed that the subjective voice quality overall severity was able to predict speech intelligibility in the noisy condition (0 dB SNR), accounting for 37% of the variance, R2 = .365, F(1,10) = 5.759, p = .037. Conclusion: Results suggest that increased background noise has a deleterious effect on the speech intelligibility of those with a voice disorder but that providing listeners with strategies in hopes of improving speaker intelligibility was not successful. Results did provide support, however, for the use of subjective voice quality ratings as a potential index of speech intelligibility.

Correlation of the sound quality and vibration of end of line testing for automatic transmission

This paper introduces a method to quickly and accurately identify the sound quality of automatic transmission in a vehicle by testing the objective vibration parameters on automatic transmission EOL (end of line) test bench. In this study, 50 automatic transmissions serve as the research object, and the vibration parameters of each gear of the transmission are measured on the EOL bench. Then, the transmission is mounted on a vehicle, the vibration parameters and acoustic parameters of corresponding working conditions are measured, and the subjective evaluation of the noise quality of the vehicle is carried out by using a grade scoring method. After this data is collected, the specific vibration parameters and acoustic parameters that are the dominant factors of sound quality are determined by correlation analysis. Based on the multiple linear regression method, the following mathematical models are formulated: the vibration parameters and the subjective evaluation of a vehicle, and the acoustic parameters and the subjective evaluation of a vehicle. Among these models, the mathematical models of the vibration parameters and the subjective evaluation of a vehicle were verified to be more accurate. The consistency between the vibration value in a vehicle and the vibration value on EOL test bench is analyzed and the EOL vibration value is determined to be the most objective evaluation of the data. After batch testing of 100 sets, the effectiveness of this model is within 95%. Finally, the value of critical amplitude of the condition of each gear of transmission is analyzed to form the judgment standard of transmission EOL vibration to ensure better sound quality for the vehicle.

Industrial Development and Social Values: an Econometric Approach

The article delves into how original institutional economics elucidates processes of industrial development through the lenses of technological, institutional, and social factors. By employing econometric methods, we can further enhance theoretical research within the realm of original institutionalism by quantitatively analyzing social values. Fundamentally, social values form the basis for selecting behavioral patterns, institutions, and institutional frameworks to shape both individual and collective development trajectories. Research on social values yields crucial insights into how the public perceives specific contemporary challenges and issues. The Veblenian dichotomy aptly conceptualizes industrial and technological transformations alongside social values and institutions. Leveraging this dichotomy draws attention to the limitations of market mechanisms driven by contracts and profit motives, underscoring the need for a comprehensive analysis of modern industrial production in light of evolving social values. Through econometric modeling, specifically the use of the ordered logit model, we investigate the impact of industrial development levels and metrics assessing institutional progress in countries, as well as the significance of technology advancement in the future. While Russia exhibits relatively lower indices reflecting both personalized and institutional trust, the model validates a correlation between respondents' subjective evaluations of institutional quality in the country and their perceptions of future technology significance. Moreover, the model confirms a positive association between a country's industrial development level and respondents' views on the importance of technology progression in the future.

Can single disease payment impact hospitalization expenses and quality in district hospital? A case study in Fujian, China

BackgroundChina is exploring payment reform methods for patients to address the escalating issue of increasing medical costs. While most district hospitals were still in the stage of Single Disease Payment (SDP) due to conditions, there is a scarcity of research on comprehensive assessment of SDP. This study aims to evaluate the implementation of SDP in a district hospital, and provided data support and scientific reference for improving SDP method and accelerating medical insurance payment reform at district hospitals.MethodsData was collected from 2337 inpatient medical records at a district hospital in Fuzhou, China from 2016 to 2021. These diagnoses principally included type 2 diabetes, planned cesarean sections, and lacunar infarction. Structural variation analysis was conducted to examine changes in the internal cost structure and dynamic shifts in medical expenses for both the insured (treatment group) and uninsured (control group) patients, pre- and post-implementation of the SDP policy on August 1, 2018. The difference-in-differences (DID) method was employed to assess changes in hospitalization expenses and quality indicators pre- and post-implementation. Furthermore, subjective evaluation of medical quality was enhanced through questionnaire surveys with 181 patients and 138 medical staff members.ResultsThe implementation of SDP decreased the medical expenses decreased significantly (P < 0.05), which can also optimize the cost structure. The drug cost ratio descended significantly, and the proportion of laboratory fee rose slightly. The changes in infection rate, cure rate, and length of stay indicated enhanced medical quality (P < 0.05). The satisfaction of inpatients with SDP was high (89.2%). Medical staff expressed an upper middle level of satisfaction (77.2%) but identified difficulties with the implementation such as “insufficient coverage of disease types”.ConclusionAfter the implementation of SDP in district hospitals, considerable progress has been achieved in restraining medical expenses, coupled with notable enhancements in both medical quality and patient satisfaction levels. However, challenges persist regarding cost structure optimization and underutilization of medical resources. This study suggests that district hospitals can expedite insurance payment reform by optimizing drug procurement policies, sharing examination information, and strengthening the management of medical records.

The association between marital satisfaction and coparenting quality: A meta-analysis.

Coparenting refers to the way parents or caregivers relate to each other as parents. Marital satisfaction (MS) is the subjective evaluation of the overall quality of one's relationship, including the extent to which needs, expectations, and desires are met. The coparenting relationship is distinct from, yet intimately connected to, the marital relationship. The objective of this study was to summarize and analyze current evidence regarding the association between MS and coparenting quality (CQ). Meta-analyses of 108 published and unpublished articles were conducted to evaluate the association between MS and CQ. In the meta-analysis of samples including both mothers and fathers, a medium association was found between MS and CQ, r = .41; 95% CI [.37, .44]; Q(88) = 1253.42. Separate meta-analyses of samples including only mothers and only fathers and an analog analysis of variance examining the moderating effect of parent gender found larger effects for mothers (r = .48) than fathers (r = .42). Maternal age, paternal education level, relationship length, and number of children were also significant moderators, with larger effects being found with families with more advanced maternal age, lower paternal education, longer relationships, and more children. The country, person reporting on CQ, and measure of CQ were also found to be statistically significant moderators. This research has implications that may inform the work of parent and family support services and contribute to positive family functioning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Improving the Safety of Computed Tomography Through Automated Quality Measurement: A Radiologist Reader Study of Radiation Dose, Image Noise, and Image Quality.

The Centers for Medicare and Medicaid Services funded the development of a computed tomography (CT) quality measure for use in pay-for-performance programs, which balances automated assessments of radiation dose with image quality to incentivize dose reduction without compromising the diagnostic utility of the tests. However, no existing quantitative method for assessing CT image quality has been validated against radiologists' image quality assessments on a large number of CT examinations. Thus to develop an automated measure of image quality, we tested the relationship between radiologists' subjective ratings of image quality with measurements of radiation dose and image noise. Board-certified, posttraining, clinically active radiologists rated the image quality of 200 diagnostic CT examinations from a set of 734, representing 14 CT categories. Examinations with significant distractions, motion, or artifact were excluded. Radiologists rated diagnostic image quality as excellent, adequate, marginally acceptable, or poor; the latter 2 were considered unacceptable for rendering diagnoses. We quantified the relationship between ratings and image noise and radiation dose, by category, by analyzing the odds of an acceptable rating per standard deviation (SD) increase in noise or geometric SD (gSD) in dose. One hundred twenty-five radiologists contributed 24,800 ratings. Most (89%) were acceptable. The odds of an examination being rated acceptable statistically significantly increased per gSD increase in dose and decreased per SD increase in noise for most categories, including routine dose head, chest, and abdomen-pelvis, which together comprise 60% of examinations performed in routine practice. For routine dose abdomen-pelvis, the most common category, each gSD increase in dose raised the odds of an acceptable rating (2.33; 95% confidence interval, 1.98-3.24), whereas each SD increase in noise decreased the odds (0.90; 0.79-0.99). For only 2 CT categories, high-dose head and neck/cervical spine, neither dose nor noise was associated with ratings. Radiation dose and image noise correlate with radiologists' image quality assessments for most CT categories, making them suitable as automated metrics in quality programs incentivizing reduction of excessive radiation doses.

Assessing risk of bias in the meta-analysis of round 1 of the Health Care Innovation Awards

BackgroundSystematic reviews of observational studies can be affected by biases that lead to under- or over-estimates of true intervention effects. Several tools have been reported in the literature that attempt to characterize potential bias. Our objective in this study was to determine the extent to which study-specific bias may have influenced intervention impacts on total costs of care (TCOC) in round 1 of the Health Care Innovation Awards.MethodsWe reviewed 82 statistical evaluations of innovation impacts on Medicare TCOC. We developed five risk-of-bias measures and assessed their influence on TCOC impacts using meta-regression.ResultsThe majority of evaluations used propensity score matching to create their comparison groups. One third of the non-randomized interventions were judged to have some risk of biased effects due largely to the way they recruited their treatment groups, and 35% had some degree of covariate imbalance remaining after propensity score adjustments. However, in the multivariable analysis of TCOC effects, none of the bias threats we examined (comparison group construction method, risk of bias, or degree of covariate imbalance) had a major impact on the magnitude of HCIA1 innovation effects. Evaluations using propensity score weighting produced larger but imprecise savings effects compared to propensity score matching.DiscussionOur results suggest that it is unlikely that HCIA1 TCOC effect sizes were systematically affected by the types of bias we considered. Assessing the risk of bias based on specific study design features is likely to be more useful for identifying problematic characteristics than the subjective quality ratings used by existing risk tools.

Evaluation of Subjective Video Quality of Television Displays

Evaluation of Subjective Video Quality of Television Displays

The Moa the Merrier: Resolving when the Dinornithiformes went extinct

The Moa (Aves: Dinornithiformes) are an extinct group of the ratite clade from New Zealand. The overkill hypothesis asserts that the first New Zealand settlers hunted the Moa to extinction by 1450 CE, whereas the staggered survival hypothesis allows for Moa survival until after Europeans began to arrive on New Zealand. Alleged Moa sightings post-1450 CE may shed light on these competing hypotheses. A dataset of 97 alleged Moa sightings from circa 1675 CE to 1993 CE was constructed, with sightings given subjective quality ratings corresponding to various statistical probabilities. Cumulative probabilities of Moa persistence were calculated with a conservative survival model using these probabilistic sighting-records; a method recently applied to sightings of the Thylacine. Cumulative persistence probability fell sharply after 1408 CE, and across pessimistic and optimistic variations of the model, it was more likely than not that the Moa were extinct by 1770 CE. Probabilistic sighting-record models favour the overkill hypothesis, and give very low probabilities of Moa persistence around the time of European arrival. Eyewitness data on Moa sightings are amenable to scientific study, and these methods may be applied to similar animals.