Clustering analysis of fatty infiltration in paraspinal muscles shows parity with semi-quantitative classification schemes by A. Pirwass, M. Munz, H.J. Wilke

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The ABC of RPV. Part II. Classification of R-parity violating signatures from UDD couplings and their coverage at the LHC by Herbi K. Dreiner, Michael Hank, Yong Sheng Koay, Martin Schürmann, Rhitaja Sengupta, Apoorva Shah, Nadja Strobbe, Evelyn Thomson

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Gender Gaps and the Gender Parity Index in Research in a Colombian Region by Isabel Cristina Rivera-Lozada, César Antonio Bonilla-Asalde, Oriana Rivera-Lozada

“…The goal of the mixed-methods approach was to determine the gender parity index (GPI) and the gender gaps in research in four fundamental aspects: research area, level of training, classification of researchers, and age groups. …”
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Altermagnetism classification by Sang-Wook Cheong, Fei-Ting Huang

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Associations among body condition score, body weight, and serum biochemistry in dairy cows by David B. Sheedy, Helen M. Golder, Sergio C. Garcia, Priyanka Reddy, Joanne E. Hemsworth, Delphine E. Vincent, Simone J. Rochfort, Jennie E. Pryce, Ian J. Lean

“…Risk of low BCS/low BW was greatest in second-parity cows, and high BCS/high BW was greatest in dry cows with greater than third parity and third-parity cows in peak milk. …”
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The impact of herd age structure on the performance of commercial sow-breeding farms by Santos Sanz-Fernández, Cipriano Díaz-Gaona, João Simões, José Carlos Casas-Rosal, Nuria Alòs, Llibertat Tusell, Raquel Quintanilla, Vicente Rodríguez-Estévez

“…Results Farms are classified into three groups according to the quadratic function fitted to the percentage of sows by parities. This classification unveils three types of herd structures: type 1 (HS1) exhibits a concave-downward trend, with a higher percentage of sows in intermediate parities (mean of 45.5% sows between the 3rd to 5th parity); type 2 (HS2) presents a trend curve that is close to a straight line, with a gradual decrease in the percentage of sows per parity (approximately 2% loss of sows census per parity); and type 3 (HS3) shows an upward concave trend curve, with an increase in the percentage of sows in later parities (19.0% of sows between 7th and ≥ 8th parity). …”
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Neural Classification of Argument Elements and Styles in Arabic Competitive Debates by Al-Zawqari Ali, Mohamed Ahmed, Abdul Gabbar Al-Sharafi, Mohammad M. Khader, Ali Safa, Gerd Vandersteen

“…Using this dataset, we form two tasks: 1) three-way rhetorical-style classification, and 2) full 13-label element detection. …”
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Fake and Real Tweet Classification Using a Pre-Trained GPT-3 Approach by Delveen Luqman Abd Alnabi

“…The findings reveal that GPT-3 exhibits remarkable parity in terms of accuracy, precision, recall, f1-score, and roc-auc, with values nearly equivalent to one, outperforming other models in this context.…”
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Utilizing Classification as a Tool to Address Gender Equality Issues in Science, Technology, and Innovation Policy by Caitlin Meyer, Du Baogui

“…Such a system has the potential to ameliorate the existing disparity, while concurrently striving to achieve gender parity within the STI landscape.…”
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A systematic review of the Robson classification for caesarean section: what works, doesn't work and how to improve it. by Ana Pilar Betrán, Nadia Vindevoghel, Joao Paulo Souza, A Metin Gülmezoglu, Maria Regina Torloni

“…The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. …”
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Cesarean delivery in a tertiary institution of the Republic of Moldova: analysis using the Robson classificationAJOG Global Reports at a Glance by Angela Marian-Pavlenco, MD, PhD, Irina Siritanu, MD, Tatiana Ribac, MD, Valentin Friptu, MD, PhD

“…CONCLUSION: The Robson Ten-Group Classification System provides a clear and detailed record of the trends in cesarean delivery. …”
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Association of Robson Ten Group Classification System with neonatal/postneonatal mortality: an analysis for the effect of the mass migrationAJOG Global Reports at a Glance by Damla Çarkçı Yıldız, MD, Elif Gül Yapar Eyi, MD

“…Conclusion: The integration of the Robson Classification with neonatal and postneonatal mortality data offers a structured method to assess cesarean section outcomes, emphasizing the significant variation in mortality rates across different Robson groups. …”
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Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information f... by A. Birkinshaw, M. Sutter, M. Nussbaum, M. Kreuzer, B. Reidy

“…The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed, and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. …”
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Population health management through human phenotype ontology with policy for ecosystem improvement by James Andrew Henry

“…The digital HPO policy advances Biological Modelling (BM) capacity and capability in a series of fair classifications. Public trust in the PHM of HPO is a vision of public health and patient safety, with a primary goal of socioeconomic success sustained by citizen privacy and trust within an ecosystem of predictor equality and intercept parity.MethodScience and technology security evaluation, resource allocation, and appropriate regulation are essential for establishing a solid foundation in a safe ecosystem. …”
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Appraisal of Linear Type Traits in Simmental Cows Reared on High Altitude of Eastern Turkey by Olcay Guler, Mete Yanar, Abdulkerim Diler, Recep Aydın, Rıdvan Kocyigit

“…A statistical model used in this research included fixed effects of stage of lactation, parity, season at time of classification and classifier. …”
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Analysis of Risk Factors Associated with the Incidence of Placenta Accreta at an Indonesian Tertiary Hospital by Aisha Grayli Cahyani, Willy Sandhika, Gatut Hardianto

“…The increased incidence of placenta accreta is contributed significantly by parity classification, a history of cesarean section, body mass index, and interbirth interval. 2. …”
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Blind Interleaver Recognition Using Deep Learning Techniques by Nayim Ahamed, Swaminathan R., B. Naveen

“…By utilizing convolutional neural network and dilation residual network models, we perform the recognition task with input data for interleavers encoded using six different channel encoders such as block, convolutional, Bose-Chaudhuri-Hocquenghem, Reed-Solomon, low density parity check, and polar. Across all cases, both convolutional neural network and dilation residual network models consistently achieve classification accuracy exceeding 95% at varying signal-to-noise ratio values. …”
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A predictive model for calculating the likelihood of recurrent uterine fibroids after surgical intervention by V. B. Tskhay, S. Zh. Badmaeva, A. N. Narkevich, I. I. Tskhay, A. V. Mikhaylova

“…When analyzing the resulting classification tree, we can conclude that the most significant factors defining the recurrence of uterine fibroids are low parity, multiple fibroids at myomectomy, intensive VEGF expression, and weak expression of BCL-2.Conclusion. …”
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Faithful novel machine learning for predicting quantum properties by Gavin Nop, Micah Mundy, Jonathan D. H. Smith, Durga Paudyal

“…Abstract Machine learning (ML) has accelerated the process of materials classification, particularly with crystal graph neural network (CGNN) architectures. …”
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Utilizing machine learning to predict the risk factors of episiotomy in parturient womenAJOG Global Reports at a Glance by Mojdeh Banaei, PhD, Nasibeh Roozbeh, PhD, Fatemeh Darsareh, PhD, Vahid Mehrnoush, MD, Mohammad Sadegh Vahidi Farashah, PhD, Farideh Montazeri, BSc

“…Each model had an AUC value assigned to it: linear regression (0.85), deep learning (0.82), support vector machine (0.79), light gradient-boosting (0.79), logistic regression (0.78), XGBoost classification (0.77), random forest classification (0.76), decision tree classification (0.75), and permutation classification—knn (0.70). …”
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