Variations In Features Research Articles

Prefrontal Cortex Codes Representations of Target Identity and Feature Uncertainty.

Many objects in the real world have features that vary over time, creating uncertainty in how they will look in the future. This uncertainty makes statistical knowledge about the likelihood of features critical to attention demanding processes such as visual search. However, little is known about how the uncertainty of visual features is integrated into predictions about search targets in the brain. In the current study, we test the idea that regions prefrontal cortex code statistical knowledge about search targets before the onset of search. Across 20 human participants (13 female; 7 male), we observe target identity in the multivariate pattern and uncertainty in the overall activation of dorsolateral prefrontal cortex (DLPFC) and inferior frontal junction (IFJ) in advance of the search display. This indicates that the target identity (mean) and uncertainty (variance) of the target distribution are coded independently within the same regions. Furthermore, once the search display appears the univariate IFJ signal scaled with the distance of the actual target from the expected mean, but more so when expected variability was low. These results inform neural theories of attention by showing how the prefrontal cortex represents both the identity and expected variability of features in service of top-down attentional control.SIGNIFICANCE STATEMENT Theories of attention and working memory posit that when we engage in complex cognitive tasks our performance is determined by how precisely we remember task-relevant information. However, in the real world the properties of objects change over time, creating uncertainty about many aspects of the task. There is currently a gap in our understanding of how neural systems represent this uncertainty and combine it with target identity information in anticipation of attention demanding cognitive tasks. In this study, we show that the prefrontal cortex represents identity and uncertainty as unique codes before task onset. These results advance theories of attention by showing that the prefrontal cortex codes both target identity and uncertainty to implement top-down attentional control.

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time

BackgroundHyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes.MethodsWe included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score.ResultsThe MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693).ConclusionsA real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.

Online Education Platform for Chinese Language Abroad Based on Remote Recognition System and Machine Learning

Abstract The remote video transmission system and machine learning are this paper’s first areas of analysis. Secondly, the remote recognition system’s image processing and content-based image retrieval are analyzed algorithmically, including image filtering, morphological processing, and canny edge extraction. The Chinese Bon platform dataset is the platform chosen for this research. The analysis of learners’ learning behavior and grade prediction is conducted based on the Chinese Bon platform dataset’s four categories: basic course information, basic learner profile, learning behavior record and learning effect. A prediction model was established for grades based on the feature variables, and its model accuracy reached 60.83%, while the accuracy of the model obtained based on the remote recognition system and machine learning algorithm was 73.35%.

In situ and ex situ variability of phenological and morphological features in Caltha palustris L. under the conditions of the West Siberian forest steppe

Background. Caltha palustris L. is known as a medicinal, food, and ornamental plant. It is a circumboreal species, and hygrohelophyte. Due to its ecological specificity, this plant can be used to study the response to an abrupt change in environments. The goal was to assess the variance in phenological and morphological features of C. palustris in the forest steppe of Western Siberia.Materials and methods. Phenotypic characters of the native population and the accessions introduced from Kemerovo Province and Altai Territory were studied. Phenological observations followed a well-known technique for herbaceous perennials. Morphometric data were processed in the PAST program using statistical indicators: the arithmetic mean with an error (M ± mM), minimum and maximum values of the trait (lim), and coefficient of variation (Cv). Significance of differences was assessed using Student’s t-test (Pt) at a 5% significance level. For traits deviating from the normal distribution, the significance of differences was assessed using the Mann–Whitney criterion (PMW) by median values of the trait (Me) at the same 5% level.Results and conclusions. The accessions varied significantly in the timing and duration of phenophases. Plants in the native population grew later and bloomed for a long time because of the variation in microconditions. Ex situ plants produced the second generation of rosette shoots and were capable of long vegetating, but the Altai accession differed in later growth and shorter phenophases. All accessions significantly differed in morphological characteristics of the shoot, except for flower size. The introduced accessions were inferior to the native ones in shoot height, leaf size, and number of flowers due to non-optimal cultivation environments. Since C. palustris demonstrated high individual and interpopulation variability, it is promising to study this species in different natural populations and select the most ornamental and sustainable accessions.

A novel deep-learning-based pressure distribution prediction approach of airfoils

Pressure distribution is a crucial flow characteristic and a key consideration in supercritical airfoil design. Traditionally, obtaining the pressure distribution involves time-consuming and computationally expensive wind tunnel experiments and computational fluid dynamics calculations. This study proposes a deep-learning-based approach to directly map input geometric information to the pressure distribution output, thereby avoiding costly wind tunnel experiments and iterative computational fluid dynamics simulations based on Navier–Stokes equations to address these challenges. Conventional surrogate models typically focus on predicting simple force factors, such as lift and drag coefficients, or require the conversion of airfoil data into images for model training. The novel approach utilizes a Variational Autoencoder for pressure distribution characteristic extraction and reconstruction from feature variables. Unlike conventional models, this approach avoids image conversion and employs a radial basis function neural network for effective mapping. The model exhibits good fitting and generalization capabilities on both training and test datasets, offering a promising solution for rapid pressure distribution prediction in airfoil design. This novel deep-learning-based approach advances airfoil design methodologies, offering significant advantages in computational efficiency and performance prediction. By directly mapping geometric information to pressure distribution, it provides an innovative and promising tool for airfoil design optimization.

Application of information fusion technology in maize fertilizer utilization experiments

Abstract The article first utilizes the hyperspectral image grayscale, texture information, and reflectance spectral information at characteristic wavelengths to establish the corresponding quantitative analysis models of nutrient content in maize plants using three modeling methods, MLR, PCR, and PLS. Then from the basis of polarized reflection features, it was inferred that the polarization degree features at sensitive wavelengths were extracted using polarized spectra and combined with chemometric techniques to achieve a quantitative analysis of the degree of nutrient stress in maize. Finally, the feature variables extracted on the hyperspectral and polarized-reflection spectral measurement systems were fused with multiple information. A diagnostic evaluation model of fertilizer utilization with polarization-hyperspectral multidimensional light information was established. The results showed that for the new slow-release fertilizers, SF1 and YNPK had higher nitrogen utilization rates, 9.91% and 7.43% higher than N1PK, respectively. And the nitrogen fertilizer utilization rate was correspondingly higher by 6%-7% in 2020 than in 2019 for each fertilizer application treatment.

Quantitative Analysis of Biodiesel Adulterants Using Raman Spectroscopy Combined with Synergy Interval Partial Least Squares (siPLS) Algorithms

Biodiesel has emerged as an alternative to traditional fuels with the aim of reducing the impact on the environment. It is produced by the esterification of oleaginous seeds, animal fats, etc., with short-chain alcohols in an alkaline solution, which is one of the most commonly used methods. This increases the oxygen content (from the fatty acids) and promotes the fuel to burn faster and more efficiently. The accurate quantification of biodiesel is of paramount importance to the fuel market due to the possibility of adulteration, which can result in economic losses, engine performance issues and environmental concerns related to corrosion. In response to achieving this goal, in this work, synergy interval partial least squares (siPLS) algorithms in combination with Raman spectroscopy are used for the quantification of the biodiesel content. Different pretreatment methods are discussed to eliminate a large amount of redundant information of the original spectrum. The siPLS technique for extracting feature variables is then used to optimize the input variables after pretreatment, in order to enhance the predictive performance of the calibration model. Finally, the D1-MSC-siPLS calibration model is constructed based on the preprocessed spectra, the selected input variables and the optimized model parameters. Compared with the feature variable selection methods of interval partial least squares (iPLS) and backward interval partial least squares (biPLS), results elucidate that the D1-MSC-siPLS calibration model is superior to the D1-MSC-biPLS and the D1-MSC-iPLS in the quantitative analysis of adulterated biodiesel. The D1-MSC-siPLS calibration model demonstrates better predictive performance compared to the full spectrum PLS model, with the optimal determination coefficient of prediction (R2P) being 0.9899; the mean relative error of prediction (MREP) decreased from 9.51% to 6.31% and the root--mean-squared error of prediction (RMSEP) decreased from 0.1912% (v/v) to 0.1367% (v/v), respectively. The above results indicate that Raman spectroscopy combined with the D1-MSC-siPLS calibration model is a feasible method for the quantitative analysis of biodiesel in adulterated hybrid fuels.

Stock Price Prediction based on LSTM and XGBoost Combination Model

In recent years, many machine learning and deep learning algorithms have been applied to stock prediction, providing a reference basis for stock trading, and LSTM neural network and XGBoost algorithm are two typical representatives, each with advantages and disadvantages in prediction. In view of this, we propose a combination model based on LSTM and XGBoost, which combines the advantages of LSTM in processing time series data and the ability of XGBoost to evaluate the importance of features. The combination model first selects feature variables with high importance through XGBoost, performs data dimensionality reduction, and then uses LSTM to make predictions. In order to verify the feasibility of the combination model, we built XGBoost, LSTM and LSTM-XGBoost models, and carried out experiments on three data sets of China Eastern Airlines, China Merchants Bank and Kweichow Moutai respectively. Finally, we concluded that the proposed LSTM-XGBoost model has good feasibility and universality in stock price prediction by comparing the accuracy of the predicted images and their performance in RMSE, RMAE, and MAPE indicators.

Innovative approaches to addressing the tradeoff between interpretability and accuracy in ship fuel consumption prediction

Ship fuel consumption is a major component of maritime transport costs and most of its emissions are harmful to the environment. Hence, it is essential to build an accurate ship fuel consumption prediction model, thereby providing reference to the navigation operations. However, maritime industry experts are wary of advanced black-box models since they cannot interpret the outcomes of these models. The application of advanced black-box models in the shipping industry remains limited and it is necessary to develop both accurate and interpretable ship fuel consumption prediction models. This study uses domain knowledge to develop two innovative methods for predicting ship fuel consumption—the first is a physics-informed neural network (PI-NN) model that improves the interpretability of the black-box model while maintaining accuracy and the second is a mixed-integer quadratic optimization (MIO) model that considers more forms of feature variable expressions in an additive white-box model. The proposed approaches address the tradeoff between model interpretability and model accuracy in ship fuel consumption prediction. The experiment results demonstrate that the PI-NN model improves the interpretability of the black-box model while preserving accuracy. The MIO model considers alternative variable expressions, leading to the flexibility of the white-box model. Finally, SHapley Additive exPlanations (SHAP) is used to explain how each feature value contributes to the predictions of the black-box model, thereby providing insights into how each value of feature variables affects fuel consumption. This study provides a solution to the tradeoff between model interpretability and model accuracy and can promote the application of data-driven models in ship fuel consumption prediction. Moreover, this study gives implications for the application of explainable machine learning models in practice.

FEATURES OF BREAST CANCER IN PATIENTS OF YOUNG AGE: SEARCH FOR DIAGNOSIS OPTIMIZATION AND PERSONALIZED TREATMENT.

The statistical data of the recent decades demonstrate a rapid growth of breast cancer (BCa) incidence and a tendency toward its increase especially in young women. In the structure of morbidity of women in the age group of 18-29 years, BCa ranks first and in the age range of 15-39 years, BCa is one of the leading causes of mortality. According to the data of the epidemiological and clinical studies, the young age is an independent unfavorable prognostic factor of BCa that is associated with an unfavorable prognosis and low survival rates and is considered an important predictor of the disease aggressiveness, a high risk of metastasis and recurrence. The variability of clinicopathological and molecular-biological features of BCa in patients of different age groups as well as the varying course of the disease and different responses to the therapy are mediated by many factors. The analysis of the literature data on the factors and mechanisms of BCa initiation in patients of different age groups demonstrates that the pathogen- esis of BCa depends not only on the molecular-genetic alterations but also on the metabolic disorders caused by the current social and household rhythm of life and nutrition peculiarities. All these factors affect both the general con- dition of the body and the formation of an aggressive microenvironment of the tumor lesion. The identified features of transcriptome and the differential gene expression give evidence of different regulations of the immune response and the metabolic processes in BCa patients of different age groups. Association between the high expression of the components of the stromal microenvironment and the inflammatory immune infiltrate as well as the increased vascu- larization of the tumor lesion has been found in BCa tissue of young patients. Proving the nature of the formation of the landscape comprising molecular-genetic, cytokine, and immune factors of the tumor microenvironment will undoubtedly contribute to our understanding of the mechanisms of tumor growth allowing for the development of algorithms for delineating the groups at high risk of tumor progression, which requires more careful monitoring and personalized treatment approach. Th s will be helpful in the development of innovative technologies for complex BCa treatment.

The influence of natural image statistics on upright orientation judgements

Humans have well-documented priors for many features present in nature that guide visual perception. Despite being putatively grounded in the statistical regularities of the environment, scene priors are frequently violated due to the inherent variability of visual features from one scene to the next. However, these repeated violations do not appreciably challenge visuo-cognitive function, necessitating the broad use of priors in conjunction with context-specific information. We investigated the trade-off between participants' internal expectations formed from both longer-term priors and those formed from immediate contextual information using a perceptual inference task and naturalistic stimuli. Notably, our task required participants to make perceptual inferences about naturalistic images using their own internal criteria, rather than making comparative judgements. Nonetheless, we show that observers' performance is well approximated by a model that makes inferences using a prior for low-level image statistics, aggregated over many images. We further show that the dependence on this prior is rapidly re-weighted against contextual information, even when misleading. Our results therefore provide insight into how apparent high-level interpretations of scene appearances follow from the most basic of perceptual processes, which are grounded in the statistics of natural images.

Machine learning prediction of biochar yield based on biomass characteristics

Slow pyrolysis is a widely used thermochemical pathway that can convert organic waste into biochar. We employed six machine learning models to predictively model 13 selected variables using pearson feature selection. Additionally, partial dependence analysis is used to reveal the deep relationship between feature variables. Both the gradient boosting decision tree and the Levenberg-Marquardt backpropagation neural network achieved training set R2 > 0.9 and testing set R2 > 0.8. But the other models displayed lower performance on the testing set, with R2 < 0.8. The partial dependence plot demonstrates that pyrolysis conditions have greater impact on biochar yield than biomass composition. Furthermore, the highest treatment temperature, being the sole consistently changing feature, can serve as a guiding factor for regulating biochar yield. This study highlights the immense potential of machine learning in experimental prediction, providing a scientific reference for reducing time and economic costs in pyrolysis experiments and process development.

A method for the early prediction of abnormal conditions in chemical processes combined with physical knowledge and the data-driven model

In a chemical process, abnormal conditions may lead to process fluctuations or unplanned shutdowns, resulting in serious economic losses and even safety accidents. Early prediction of abnormal conditions can provide sufficient response time for operators to maintain the smooth operation of the device. This paper proposes an early prediction method for abnormal conditions in chemical processes combining physical knowledge and the data-driven model, which effectively enhances the model's generalizability and interpretability. Firstly, the key variable of abnormal conditions is determined based on physical knowledge. Then, the Spearman ranking correlation coefficient (SRCC) is utilized to extract feature variables related to the key variable. Next, a multivariate time series forecasting model combining long short-term memory (LSTM) and gated recurrent unit (GRU) is constructed to predict future trends of key variable data. Finally, taking the abnormal condition of crude oil with water in the crude unit (CU) as an example, the proposed method is successfully applied, showing better prediction performance and providing operators with sufficient time to take action.

Granulation-based LSTM-RF combination model for hourly sea surface temperature prediction

ABSTRACT Accurate predictions of sea surface temperature (SST) are crucial due to the significant impact of SST on the global ocean-atmospheric system and its potential to trigger extreme weather events. Many existing machine-learning-based SST predictions adapt the traditional iterative point-wise prediction mechanism, whose predicting horizons and accuracy are limited owing to the high sensitivity to cumulative errors during iterative predictions. Therefore, this paper proposes a novel granulation-based long short-term memory (LSTM)-random forest (RF) combination model that can fully capture the feature dependencies involved in the fluctuation of SST sequences, reduce the cumulative error in the iteration process, and extend the prediction horizons, which includes two sub-models (adaptive granulation model and hybrid prediction model). They can restack the one-dimensional SST time-series into multidimensional feature variables, and achieve a strong forecasting ability. The analysis shows that the proposed model can achieve more accurate prediction-hours in nearly all prediction ranges from 1 to 125 h. The average prediction error of the proposed model in 25–125 h is 0.07 K, similar to that (0.067 K) in the first 24 h, which exhibits a high generalization performance and robustness and isthus a promising platform for the medium- and long-term forecasting of hourly SSTs.

Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short‐term memory network

AbstractHistorical tourism volume, search engine data, and weather calendar data have close causal relationship with daily tourism volume. However, when used in the prediction of daily tourism volume, the feature variables of the huge and complex search engine data do not have strong independence. These repetitive and highly relevant data must be analyzed and selected; otherwise, they will increase the training burden of neural network and reduce the prediction effect. This study proposes a daily tourism volume prediction model, maximum correlation minimum redundancy feature selection and long short‐term memory, on the basis of feature selection and deep learning. Firstly, the multivariate high‐dimensional features, including search engine data and weather factors, are selected to identify the key influencing factors. Secondly, the deep neural network is used to make a multistep forward rolling prediction of daily tourism volume. Results show that keywords of famous scenic spots, weather, historical tourism volume, and tourism strategies in the search engine data significantly improve the prediction accuracy of daily tourism volume. The proposed maximum correlation minimum redundancy feature selection and long short‐term memory model performs better than other models, such as autoregressive integrated moving average, multiple regression, support vector machine, and long short‐term memory.

The role of LightGBM model in management efficiency enhancement of listed agricultural companies

Abstract This paper first explores the principle of the the LightGBM model, which uses a gradient-based one-sided sampling GOSS procedure to sample the sample data to ensure the accuracy of the procedure in information gain evaluation. The mutually exclusive feature bundling Histogram procedure is used to bind the feature variables of the data and the Leaf-wise strategy for differential splitting to enhance the training speed of the model. Then the steps of the LightGBM procedure are discussed, using a a decision tree as the base learner, then iterative training in sequence, and greedy learning using forward distributed procedure and the original feature fetches must be identified from the bundled features to reduce the training complexity. Finally, the LightGBM procedure is associated and investigated with the other four procedures in terms of discriminative accuracy and discriminative time of the factors affecting the efficiency of enterprise management. In terms of level discrimination accuracy, the level discrimination rate of the LightGBM model is basically above 0.7, and the discrimination rate reaches 0.9 at level 6, which is better than other procedures. The discriminatory time of the LightGBM model is only 28 seconds, which is about 16 times faster than the CART model and 20 times faster than the support vector machine model.

Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression

BackgroundHLA-B27 positivity is normal in patients undergoing rheumatic diseases. The diagnosis of many diseases requires an HLA-B27 examination.MethodsThis study screened totally 1503 patients who underwent HLA-B27 examination, liver/kidney function tests, and complete blood routine examination in First Affiliated Hospital of Guangxi Medical University. The training cohort included 509 cases with HLA-B27 positivity whereas 611 with HLA-B27 negativity. In addition, validation cohort included 147 cases with HLA-B27 positivity whereas 236 with HLA-B27 negativity. In this study, 3 ML approaches, namely, LASSO, support vector machine (SVM) recursive feature elimination and random forest, were adopted for screening feature variables. Subsequently, to acquire the prediction model, the intersection was selected. Finally, differences among 148 cases with HLA-B27 positivity and negativity suffering from ankylosing spondylitis (AS) were investigated.ResultsSix factors, namely red blood cell count, human major compatibility complex, mean platelet volume, albumin/globulin ratio (ALB/GLB), prealbumin, and bicarbonate radical, were chosen with the aim of constructing the diagnostic nomogram using ML methods. For training queue, nomogram curve exhibited the value of area under the curve (AUC) of 0.8254496, and C-value of the model was 0.825. Moreover, nomogram C-value of the validation queue was 0.853, and the AUC value was 0.852675. Furthermore, a significant decrease in the ALB/GLB was noted among cases with HLA-B27 positivity and AS cases.ConclusionTo conclude, the proposed ML model can effectively predict HLA-B27 and help doctors in the diagnosis of various immune diseases.

Characterization of an induced pluripotent stem cell line NCHi011-A from a 23-year-old female with Alagille Syndrome harboring a heterozygous JAG1 pathogenic variant

Alagille syndrome (ALGS) is a multisystem disease with high variability in clinical features. ALGS is predominantly caused by pathogenic variants in the Notch ligand JAG1. An iPSC line, NCHi011-A, was generated from a ALGS patient with complex cardiac phenotypes consisting of pulmonic valve and branch pulmonary artery stenosis. NCHi011-A is heterozygous for a single base duplication causing a frameshift in the JAG1 gene. This iPSC line demonstrates normal cellular morphology, expression of pluripotency markers, trilineage differentiation potential, and identity to the source patient. NCHi011-A provides a resource for modeling ALGS and investigating the role of Notch signaling in the disease.

Real-time intention prediction for upper limb configuration coordinates in reaching motions

Surface electromyography (sEMG) signals have great potential for predicting upper limb motion. Although prior investigations have explored diverse applications of sEMG signal analysis, but few studies have focused on real-time motion prediction within the context of upper limb configuration space. Additionally, previous research has not adequately considered individual variability in sEMG features. This study aims to accomplish two main objectives. Firstly, it seeks to examine the dissimilarities in signal distribution across different subjects when employing various features. Additionally, the study aims to establish a correlation between signal distribution patterns and the model’s predictive accuracy. Secondly, the study introduced a personalized standardization (PSD) technique, which will serve to normalize the shape of the signal distribution across different subjects, thereby addressing the inter-individual differences in sEMG features. A bi-directional long short-term memory (Bi-LSTM) network is employed to estimate the real-time moving intention of the upper limb after applying the PSD technique. The analysis of signal distribution involved nine combinations of features, encompassing six features, namely mean absolute value (MAV), wave length (WL), variance (VAR), root mean square (RMS), mean frequency (MNF) and median frequency (MDF). To assess predictive capabilities, several models were evaluated. Remarkably, the distribution analysis clearly demonstrated that the shape of the signal distribution notably influences the model’s performance. Accroding to results, the incorporation of the PSD technique resulted in a notable improvement in the accuracy of the Bi-LSTM model, which leds to an enhancement of up to 2.8 percentage points in predictive accuracy. Additionally, the Bi-LSTM model emerged as the highest-performing model among all the compared models during the analysis. These findings underscore the importance of considering individual variability in sEMG features when developing predictive models for upper limb motion and highlight the potential benefits of employing the PSD technique to enhance model performance.

Record of palaeoclimate across theCretaceous–Palaeogeneboundary from palaeosols in the west‐central San Juan Basin, New Mexico, USA

AbstractThe mass extinction at the Cretaceous–Palaeogene boundary is widely attributed to sudden and severe climate changes forced by bolide impact and/or flood basalt volcanism. In terrestrial depositional settings, these changes may potentially be recorded by palaeosols. To test the ability of pedogenic features to record both long‐term climate and shorter‐term changes preceding and following the Cretaceous–Palaeogene extinction event, palaeosols in the Upper Cretaceous (Maastrichtian) Naashoibito Member of the Ojo Alamo Formation and the lower Palaeocene (Danian) Nacimiento Formation in the San Juan Basin of north‐western New Mexico, USA, were examined, including data from previous studies. The fine‐grained facies of the Naashoibito Member comprises grey to greenish‐grey and red‐banded mudstones displaying pedogenic features including colour mottling, root traces, cutans, ped fabrics, pedogenic slickensides and calcareous nodules. Aside from a high‐chroma horizon at the formation base, palaeosols in the lower Nacimiento Formation are broadly similar to those observed in the Naashoibito Member. Lateral and vertical variability of the pedogenic features between correlated sections suggest that soil hydrology varied spatially and temporally from very saturated to seasonally well‐drained, with temporal variations controlled by basin sedimentation rates. Abrupt and/or catastrophic climate events precisely at the Cretaceous–Palaeogene boundary are not recorded due to an unconformity at the top of the Naashoibito Member. However, the presence of kaolinite in the clay mineral assemblage of the Nacimiento Formation, particularly near the formation base, but not in the Naashoibito Member, indicates episodic warmth and short (104 years) intervals of more intense weathering conditions during the very early Danian as compared to the late Maastrichtian. Aside from short warm intervals, the overall palaeoclimate during deposition of both formations was warm and consistently subhumid to humid and seasonal, suggesting no substantial long‐term (105–106) climate change took place across the Cretaceous–Palaeogene boundary in the San Juan Basin.