Quantification Process Research Articles

Three-dimensional Quantitative Standards for Assessing Outcomes of Facial Lipotransfer: A Review.

Reliable quantitative data are required to address the unpredictability of facial autologous fat grafting (AFG). Facial evaluation by 3D scanning technology is getting popular. However, this process lacks unified standards and the reliability assessments. This study aimed to summarize a set of standards to improve the 3D quantified reliability of AFG outcomes. A systematic review was used to collect the differences in and limitations of 3D assessments and analyze the effect of the quantification process on the AFG outcomes. Healthy subjects undergoing only one facial structural AFG and 3D assessments were included. The revealed specific issues guided the subsequent narrative review that involves 3D measurement and fat volume retention rate (FVRR) analysis. Criteria were formulated based on the narrative review. The systematic review revealed the quantitative process to be operator-dependent. The intra-group FVRR in the postoperative 11+ month group varied significantly (P=.03). The review identified a set of 3D measurement standards, including two optimal software products, two necessary steps for preprocessing, and four testing criteria. We proposed a new calculation formula and parameter and recommended a segmental area analysis for assessing the outcomes of full-face fat grafting. As far as the 3D evaluation of AFG outcomes is concerned, this is the first study to comprehensively analyze the process and set quantitative criteria. These standards would not only guide future research more reliably, but also provide fresh insight into the review of the past research. 3D measurement standards also apply to all face-related studies requiring 3D registration. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Machine learning accurately quantifies epicardial adipose tissue from non-contrast CT images in coronary artery disease

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Industry Alignment Fund – Pre-positioning Programme Background Epicardial adipose tissue (EAT) is the visceral fat deposit within the pericardium that surrounds the heart and the coronary arteries. EAT volume measured from non-contrast CT (NCCT) has been demonstrated to be significantly associated with adverse cardiovascular risk,1 particularly in patients with coronary artery disease.2 However, routine measurement of EAT volume is still challenging in clinical practice, as it is a tedious manual process and prone to human error. Purpose We aimed to develop a fully automated AI toolkit (i.e., AI EAT) for the quantification of EAT from routine NCCT scans and assess its performance in reference to clinical ground truth. Methods This is a multicenter study which performs CT scans in 5000 Asian Admixture patients (APOLLO study NCT05509010). In the current stage of this study, NCCT data analysis were conducted in 551 patients with 26,037 images. AI EAT was developed via a novel deep learning framework using an ensemble region-based UNet. The region-based UNet uses 2 component UNet models to perform segmentation of pericardium at the apex region and non-apex region (middle and basal). EAT volume was obtained by automated thresholding of the voxels (-190 to -30 Hounsfield Unit) within the pericardium (Figure 1). The network was trained in 501 patients with 23,712 NCCT images and tested in 50 patients with 2,325 NCCT images. The performance of AI EAT was evaluated with respect to clinical ground truth using Dice similarity coefficient (DSC), Pearson correlation, and Bland-Altman analysis. Results The AI EAT quantification process took less than 10 seconds per subject, compared with 20-30 minutes for expert readers. Compared to clinical ground truth, our AI EAT achieved a DSC of 0.96±0.01 and 0.91±0.02 for pericardium and EAT segmentations, respectively. There was strong agreement between the AI EAT and clinical ground truth in deriving the EAT volume (r=0.99, P<0.001) with minimal error of 7±5%. Conclusion End-to-end deep learning system accurately quantifies epicardial adipose tissue in standard NCCT images without manual segmentation.

Measurement of Coupling Coordination Between Vocational Happiness and Affective Education Ability of Young Teachers

When teachers are giving affective education, their emotional communication with students is a kind of state or process during which teachers’ work pressure can be alleviated when they are cultivating students’ sentiment, in the meantime, they also could attain positive mental quality and a sense of vocational happiness. Regarding the relationship between young teachers’ vocational happiness and their Affective Education Ability (AEA), only 1-2 articles have discussed it from the perspective of a single ability, there isn’t complete research on the AEA system of teachers. Therefore, to fill in this research blank, this paper aims to measure the coupling and coordination between the development of young teachers’ vocational happiness and their AEA, in the hopes of providing a piece of useful evidence for relevant research. The theory of emotional intelligence and positive psychology serve as the theoretical basis of this paper. In the second chapter, this paper builds a framework for young teachers’ affective education and an AEA training model, and employs Artificial Neural Network (ANN) to extract the time series data features of the divided six evaluation indexes of young teachers’ AEA. In the third chapter, this paper uses the Coupling Coordination Degree (CCD) to measure the coupling and coordination of the development level young teachers’ vocational happiness and their six kinds of AEA, and gives the quantification process. In the fourth chapter, this paper adopts the Grey Relational Analysis (GRA) to quantify the influence degree of all indexes on young teachers’ vocational happiness and their six kinds of AEA, thereby realizing the quantitative measurement of each evaluation index on the comprehensive development of the two. At last, experimental results are drawn to verify the measurement and analysis results of the coupling and coordination of the two.

Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm

Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm’s efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.

The semi-automated development of plant cell wall finite element models

This study presents a methodology for a high-throughput digitization and quantification process of plant cell walls characterization, including the automated development of two-dimensional finite element models. Custom algorithms based on machine learning can also analyze the cellular microstructure for phenotypes such as cell size, cell wall curvature, and cell wall orientation. To demonstrate the utility of these models, a series of compound microscope images of both herbaceous and woody representatives were observed and processed. In addition, parametric analyses were performed on the resulting finite element models. Sensitivity analyses of the structural stiffness of the resulting tissue based on the cell wall elastic modulus and the cell wall thickness; demonstrated that the cell wall thickness has a three-fold larger impact of tissue stiffness than cell wall elastic modulus.

The rise of the reflexive expert? Epistemic, care-ful and instrumental reflexivity in global public policy

The production of data and numbers has become the key mechanism of both knowing and governing global public policy. And yet, processes of quantification are inherently paradoxical: from expectations of technocratic rationality and political usability of producing ‘global’ numbers that count for ‘local’ politics and needs to practical limitation of measurement and the necessity to work with ‘good enough’ data. This begs a question – how do these competing epistemic, political and value orders manifest themselves through the work that experts do? In this article, we explore the problem by focussing on reflexivity as a way for experts (primarily those working in key International Organisations) to make sense of and tame the tensions inherent in their work. Through rich qualitative exploration of over 80 semi-structured interviews with experts working in the areas of poverty, education and statistical capacity development, we contribute to debates in the social studies of quantification by arguing that reflexivity is not just a mental process that experts engage in but rather an important resource allowing them to make sense of the contradictions inherent in their work and to mobilise political and ethical considerations in the technocratic process of producing numbers. We identify three types of reflexivity: (1) epistemic reflexivity – regarding the quality of data and its epistemic status as reflecting the reality; (2) care-ful reflexivity – regarding values embedded in data and the duty of care to the populations affected by the measurement and (3) instrumental reflexivity – regarding political rationality and necessary trade-off required to realise political goals. Overall, the article argues that reflexivity becomes an increasingly central expert practice, allowing the transformation of the process of quantification into one of qualification enabling them to attach political attributes and values to data and measurement.

Continuous organ perfusion monitoring using indocyanine green in a piglet model.

Unrecognized organ hypoperfusion may cause major postoperative complications with detrimental effects for the patient. The use of Indocyanine Green (ICG) to detect organ hypoperfusion is emerging but the optimal methodology is still uncertain. The purpose of this study was to determine the feasibility of real-time continuous quantitative perfusion assessment with Indocyanine Green (ICG) to monitor organ perfusion during minimally invasive surgery using a novel ICG dosing regimen and quantification software. In this experimental porcine study, twelve subjects were administered a priming dose of ICG, followed by a regimen of high-frequency (1 dose per minute), low-dose bolus injections with weight-adjusted (0.008mg/kg) ICG allowing for continuous perfusion monitoring. In each pig, one randomly assigned organ of interest [stomach (n = 3), ascending colon (n = 3), rectum (n = 3) and spleen (n = 3)] was investigated with varying camera conditions. Video recording was performed with the 1588 AIM Stryker camera platform and subsequent quantitative analysis of the ICG signal were performed using a research version of a commercially available surgical real-time analysis software. Using a high-frequency, low-dose bolus ICG regimen, fluorescence visualization and quantification in abdominal organs were successful in the stomach (3/3), ascending colon (1/3), rectum (2/3), and the spleen (3/3). ICG accumulation in the tissue over time did not affect the quantification process. Considerable variation in fluorescence signal was observed between organs and between the same organ in different subjects. Of the different camera conditions investigated, the highest signal was achieved when the camera was placed 7.5cm from the target organ. This proof-of-concept study finds that real-time continuous perfusion monitoring in different abdominal organs using ICG is feasible. However, the study also finds a large variation in fluorescence intensity between organs and between the same organ in different subjects while using a fixed weight-adjusted dosing regimen using the same camera setting and placement.

COMBINED DATA AND DEEP LEARNING MODEL UNCERTAINTIES: AN APPLICATION TO THE MEASUREMENT OF SOLID FUEL REGRESSION RATE

In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have uncertainties originating from multiple sources, combining these in a systematic way for quantities of interest (QoI) remains a challenge. In this paper, we present a forward propagation uncertainty quantification (UQ) process to produce a probabilistic distribution for the observed regression rate r. We characterized two input data uncertainty sources from the experiment (the distortion from the camera <i>U</i><sub>c</sub> and the non-zero-angle fuel placement <i>U</i><sub>Y</sub>), the prediction and model form uncertainty from the deep neural network (<i>U</i><sub>m</sub>), as well as the variability from the manually segmented images used for training it (<i>U</i><sub>s</sub>). We conducted seven case studies on combinations of these uncertainty sources with the model form uncertainty. The main contribution of this paper is the investigation and inclusion of the experimental image data uncertainties involved, and how to include them in a workflow when the QoI is the result of multiple sequential processes.

The Allegory of the Rock Engineering Cave

Geoscientists and engineers are part of a system that produces changes to the world we live in. As machines are expected to gradually replace humans in various technical tasks (e.g., data collection and data characterisation), it becomes crucial to apply critical thinking and to question the foundations of commonly accepted practices. The challenge is to accept that empirical methods are shaped by cognitive biases, which result from our mind interpreting data by a process of data simplification. Indeed, it is possible to draw an analogy between rock engineering methods and Plato’s Allegory of the Cave. The fire casting the shadows along the cave walls represents the process of quantification of qualitative assessments of commonly accepted data collection methods. The chains holding the engineers as prisoners in the cave are empirical methods accepted as industry standards despite important limitations. Engineering judgment alone will not allow engineers to break free of those chains, and to emerge from the confined spaces of the cave and see things for what they really are (i.e., introduce truly objective data collections methods that better reflect failure mechanisms). In this paper the authors use philosophical arguments to justify the need to “dequantify” the GSI classification system and to teach us that engineering design should always be driven by the important questions of how, why, and whether.

Behavioral Biases in the Uncertainty Quantification Process

Behavioral Biases in the Uncertainty Quantification Process

Cognitive Complexity Applied to Software Development: An Automated Procedure to Reduce the Comprehension Effort

The cognitive complexity of a software application determines the amount of human effort required to comprehend its internal logic, which results in a subjective measurement. The quantification process of the cognitive complexity as a metric is problematic since the factors representing the computation do not represent the exact human cognition. Therefore, the determination of cognitive complexity requires expansion beyond its quantification. The human comprehension effort related with a software application is associated with each phase of its development process. Correct requirements identification and accurate logical diagram generation prior to code implementation can lead to proper logical identification of software applications. Moreover, human comprehension is essential for software maintenance. Defect identification, correction and handling of code quality issues cannot be maintained without good comprehension. Therefore, cognitive complexity can be effectively applied to demonstrate human understandability inside the respective phases of requirements analysis, design, defect tracking, and code quality optimization. This study involved automation of the above-mentioned phases to reduce the manual human cognitive load and reduce cognitive complexity. It was found that the proposed system could enhance the average accuracy of requirements analysis and class diagram generation by 14.44% and 9.89% average accuracy incrementation through defect tracking and code quality issues compared to manual procedures.

Fast generation of calculated ADF-EDX scattering cross-sections under channelling conditions

Advanced materials often consist of multiple elements which are arranged in a complicated structure. Quantitative scanning transmission electron microscopy is useful to determine the composition and thickness of nanostructures at the atomic scale. However, significant difficulties remain to quantify mixed columns by comparing the resulting atomic resolution images and spectroscopy data with multislice simulations where dynamic scattering needs to be taken into account. The combination of the computationally intensive nature of these simulations and the enormous amount of possible mixed column configurations for a given composition indeed severely hamper the quantification process. To overcome these challenges, we here report the development of an incoherent non-linear method for the fast prediction of ADF-EDX scattering cross-sections of mixed columns under channelling conditions. We first explain the origin of the ADF and EDX incoherence from scattering physics suggesting a linear dependence between those two signals in the case of a high-angle ADF detector. Taking EDX as a perfect incoherent reference mode, we quantitatively examine the ADF longitudinal incoherence under different microscope conditions using multislice simulations. Based on incoherent imaging, the atomic lensing model previously developed for ADF is now expanded to EDX, which yields ADF-EDX scattering cross-section predictions in good agreement with multislice simulations for mixed columns in a core–shell nanoparticle and a high entropy alloy. The fast and accurate prediction of ADF-EDX scattering cross-sections opens up new opportunities to explore the wide range of ordering possibilities of heterogeneous materials with multiple elements.

Modeling deadwood for rockfall mitigation assessments in windthrow areas

Abstract. Studying how deadwood mitigates the rockfall hazard in mountain forests is key to understanding the influence of climate-induced disturbances on the protective capacity of mountain forests. Both experimental quantification and numerical process modeling are needed to address this question. Modeling provides detailed insights into the rock–deadwood interaction and can therefore be used to develop effective forest management strategies. Here, we introduce an automatic deadwood generator (ADG) for assessing the impact of fresh woody storm debris on the protective capacity of a forest stand against rockfall. The creation of various deadwood scenarios allows us to directly quantify the mitigation potential of deadwood. To demonstrate the functionality of the proposed ADG method, we compare deadwood log patterns, deadwood effective height, and mesoscale surface ruggedness observed in field surveys in a natural windthrow area with their simulated counterparts. Specifically, we consider two sites near Lake Klöntal, Switzerland, where a major windthrow event occurred in 2019. We perform rockfall simulations for the time (a) before, (b) directly after, and (c) 10 years after the windthrow event. We further compare the results with (d) a simulation with complete clearing of the thrown wood: in other words, a scenario with no standing forest remaining. We showcase an integration of deadwood into rockfall simulations with realistic deadwood configurations alongside a diameter at breast height (DBH)- and rot-fungi-dependent maximum deadwood breaking energy. Our results confirm the mitigation effect of deadwood, which significantly reduces the jump heights and velocities of 400 kg rocks. Our modeling results suggest that, even a decade after the windthrow event, deadwood has a stronger protective effect against rockfall than that provided by standing trees. We conclude that an ADG can contribute to the decision-making involved in forest and deadwood management after disturbances.

Quantification Methodology of Ammonia Produced from Electrocatalytic and Photocatalytic Nitrogen/Nitrate Reduction

Nitrogen reduction reaction (NRR) and nitrate reduction reaction (NO3−RR) provide a potential sustainable route by which to produce ammonia, a next-generation energy carrier. Many studies have been conducted over the years, mainly emphasizing material design and strategies to improve catalytic performance. Despite significant achievements in material design and corresponding fundamental knowledge, the produced ammonia is still very limited, which makes it prone to bias. The presence of interferants (e.g., cations and sacrificial reagents), the pH of the solution, and improper analytical procedure can lead to the over or underestimation of ammonia quantification. Therefore, the selection of the appropriate ammonia quantification method, which meets the sample solution condition, along with the proper analytical procedures, is of great importance. In this review, the state-of-the-art ammonia quantification method is summarized, emphasizing the advantages, limitations, and practicality for NRR and NO3−RR studies. Fundamental knowledge of the quantification method is introduced. Perspective on the considerations for selecting the suitable quantification method and for performing the quantification process is also provided. Although non exhaustive, this focused review can be useful as a guide to design the experimental setup and procedure for more reliable ammonia quantification results.

X-ray fluorescence spectroscopy (XRF) for metallome analysis of herbarium specimens

Background“Herbarium X-ray Fluorescence (XRF) Ionomics” is a new quantitative approach for extracting the elemental concentrations from herbarium specimens using handheld XRF devices. These instruments are principally designed for dense sample material of infinite thickness (such as rock or soil powder), and their built-in algorithms and factory calibrations perform poorly on the thin dry plant leaves encountered in herbaria. While empirical calibrations have been used for ‘correcting’ measured XRF values post hoc, this approach has major shortcomings. As such, a universal independent data analysis pipeline permitting full control and transparency throughout the quantification process is highly desirable. Here we have developed such a pipeline based on Dynamic Analysis as implemented in the GeoPIXE package, employing a Fundamental Parameters approach requiring only a description of the measurement hardware and derivation of the sample areal density, based on a universal standard.ResultsThe new pipeline was tested on potassium, calcium, manganese, iron, cobalt, nickel, and zinc concentrations in dry plant leaves. The Dynamic Analysis method can correct for complex X-ray interactions and performs better than both the built-in instrument algorithms and the empirical calibration approach. The new pipeline is also able to identify and quantify elements that are not detected and reported by the device built-in algorithms and provides good estimates of elemental concentrations where empirical calibrations are not straightforward.ConclusionsThe new pipeline for processing XRF data of herbarium specimens has a greater accuracy and is more robust than the device built-in algorithms and empirical calibrations. It also gives access to all elements detected in the XRF spectrum. The new analysis pipeline has made Herbarium XRF approach even more powerful to study the metallome of existing plant collections.

Implementation of Multidisciplinary and Multifidelity Uncertainty Quantification Methods for Sonic Boom Prediction

To advance a supersonic aircraft design process, an uncertainty quantification study is conducted for sonic boom prediction while considering uncertainties associated with flight and atmospheric conditions. The uncertainty quantification process is implemented within a multidisciplinary analyses framework and assisted with a multifidelity surrogate model based approach. The sonic boom prediction framework requires input from the flowfield pressure distribution solution to generate the near-field pressure signature of the aircraft, which is then propagated throughout the atmosphere to the ground by using aeroacoustic methods. The open-source SU2 suite is employed as a high-fidelity flow solver tool to obtain the aerodynamic solution, while in-house postprocessing scripts are developed to generate the required near-field pressure signature. For low-fidelity flow analysis, A502 PAN AIR, a higher-order panel code which solves flows around slender bodies in low angles of attack for subsonic and supersonic regimes, is used. For nonlinear aeroacoustic propagation, NASA Langley Research Center’s code sBOOM exploits the near-field pressure signature for both high-fidelity and low-fidelity sonic boom calculations. Efficient uncertainty quantification tools are developed in house by implementing multifidelity polynomial chaos expansion and multifidelity Monte Carlo methods. Several flight and atmospheric parameters are selected to include randomness where these uncertainties are propagated into the sonic boom loudness prediction of the JAXA wing-body model, which is a low boom aircraft. Finally, an overall assessment of the multifidelity uncertainty quantification methods is presented in terms of efficiency and numerical accuracy.

Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training.

Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role in furthering our understanding of the cellular and molecular mechanisms of injury and developing new treatment strategies for clinical translation. At present, the quantification of neurons in histological images consists of slow, manually intensive morphological assessment, requiring many repeats by an expert, which can prove to be time-consuming and prone to human error. Hence, there is an urgent need to automate the neuron classification and quantification process. In this article, we present a 'Gradient Direction, Grey level Co-occurrence Matrix' (GD-GLCM) image training method which outperforms and simplifies the standard training methodology using texture analysis to cell-classification. This is achieved by determining the Grey level Co-occurrence Matrix of the gradient direction of a cell image followed by direct passing to a classifier in the form of a Multilayer Perceptron (MLP). Hence, avoiding all texture feature computation steps. The proposed MLP is trained on both healthy and dying neurons that are manually identified by an expert and validated on unseen hypoxic-ischemic brain slice images from the fetal sheep in utero model. We compared the performance of our classifier using the gradient magnitude dataset as well as the gradient direction dataset. We also compare the performance of a perceptron, a 1-layer MLP, and a 2-layer MLP to each other. We demonstrate here a way of accurately identifying both healthy and dying cortical neurons obtained from brain slice images of the fetal sheep model under global hypoxia to high precision by identifying the most minimised MLP architecture, minimised input space (GLCM size) and minimised training data (GLCM representations) to achieve the highest performance over the standard methodology.

Use of GIS to Discover the Existence of Terrorism

Threats to national defense can be military or non-military. The greatest unresolved threat and challenge facing the Indonesian state is terrorism. The Indonesian government has dealt with terrorism, but catching terrorists remains difficult. The purpose of this research is to provide an alternative that uses Geospatial Intelligence (GEOINT) to find out where terrorists are hiding. The limitation of this research is the mountainous region in Central Sulawesi Province. The method used in this study is to use the GEOINT approach which is a combination of remote sensing, geographic information systems (GIS) and cartography, to extract information and analyze the results. The analysis was performed using a weighted linear combination method. The quantification process is carried out on all spatial data used for each parameter related to the presence of terrorists in the mountains. Quantification is done by changing each sub-parameter class to a value between 1-5. Each value is then weighted as a coefficient to arrive at the final score. From the results of the analysis and discussion it can be concluded that the GEOINT analysis can be used as an initial research on terrorist hideouts.

Improving Iron and Folic Acid Supplementation Among Pregnant Women: An Implementation Science Approach in East-Central Uganda.

To address maternal iron-deficiency anemia and low uptake of iron and folic acid supplementation (IFAS) among antenatal care (ANC) clinic attendees in East-Central Uganda, the Anemia Implementation Science Initiative embedded enhanced quality improvement (QI) activities into an integrated health project utilizing QI methodologies. To address 2 bottlenecks of stock-outs and inadequate health education for pregnant women during ANC, an enhanced QI intervention was implemented from July 2019 to September 2020 in 2 districts. We conducted a mixed-methods effectiveness quasi-experimental study to assess whether the intervention increased the availability of IFAS in the intervention districts. We used longitudinal facility-level data from 2 treatment districts and 1 comparison district for the quantitative results. Difference-in-difference estimation was used to measure the impact of the intervention on IFAS health education and IFA availability at the health facility. We used logistic regression modeling to control for factors associated with IFAS uptake and potential differences in baseline values. Researchers conducted exit interviews with ANC clients and in-depth interviews with providers and district managers for greater insights into the implementation process. The intervention increased the probability, at a statistically significant level, of pregnant women both receiving IFAS and receiving health education on IFAS during ANC. According to inter-viewees, the intervention approach improved stakeholder engagement and buy-in, which brought about change at all levels of the health system. The intervention successfully addressed the 2 main bottlenecks to availability of IFAS for pregnant women attending ANC-inadequate provision of IFAS education and a weak drug quantification process. Even without additional funds to purchase commodities, this approach improved district capacity to advocate for and manage IFAS commodities. It could also be used to strengthen overall ANC quality.

Why Engineers Should Not Attempt to Quantify GSI

In the past decade, there has been an increasing trend of digitalizing rock engineering processes. However, this process has not been accompanied by a critical analysis of the very same empirical methods that many complex numerical and digital methods are founded upon. As engineers, we are taught to use and trust numbers. Indeed, we would not be able to define the factor of the safety of a structure without numbers. However, what happens when those numbers are nothing but numerical descriptions of qualitative assessments? In this paper we present a critical review of the many attempts presented in the literature to quantify GSI (geological strength index). To the authors’ knowledge, this paper represents the first time that all the different GSI tables and quantification methods that have been proposed over the past two decades are collated and compared critically. In our critique, we argue against the paradigm whereby the quantification process adds the experience factor for inexperienced engineers. Furthermore, we discuss the limitations of the notion that GSI quantification methods could transform subjectivity into objectivity since the parameters under considerations are not quantitative measurements. Relying on empirically defined quantitative equivalences raises important questions, particularly when these quantitative equivalences are being used to define so-called accurate rock mass classification input for design purposes.