# CatBoost

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Journal of Intelligent Media Computing Sep 28, 2026
Extracting heart girth and body length from 2D images with age and sex, XGBoost predicts body weight in unseen Surti goats at R²=0.7955
This study used Mask R-CNN to segment goats from two-dimensional images and extract heart girth and body length, combined these with age and sex metadata, trained XGBoost, Random Forest and CatBoost and integrated them with weights of 0.5/0.3/0.2; on a validation set split by Animal ID (37 goats) XGBoost reached R²=0.8514 (RMSE=4.26 kg, MAE=3.47 kg, MAPE=16.27%), and on a completely unseen test set (37 goats) XGBoost performed best with R²=0.7955 (MSE=29.15 kg², RMSE=5.40 kg, MAE=4.01 kg, MAPE=16.38%) while the weighted ensemble recorded R²=0.7713 (MSE=32.60 kg², RMSE=5.71 kg, MAE=4.13 kg, MAPE=16.60%), indicating that combining visual and morphometric information under strict group-based evaluation provides reliable non-contact body weight estimation on unseen livestock.
This study used Mask R-CNN to segment goats from two-dimensional images and extract heart girth and body length, combined these with age and sex metadata, trained XGBoost, Random Forest and CatBoost and integrated them with weights of 0.5/0.3/0.2; on a validation set split by Animal ID (37 goats) XGBoost reached R²=0.8514 (RMSE=4.26 kg, MAE=3.47 kg, MAPE=16.27%), and on a completely unseen test set (37 goats) XGBoost performed best with R²=0.7955 (MSE=29.15 kg², RMSE=5.40 kg, MAE=4.01 kg, MAPE=16.38%) while the weighted ensemble recorded R²=0.7713 (MSE=32.60 kg², RMSE=5.71 kg, MAE=4.13 kg, MAPE=16.60%), indicating that combining visual and morphometric information under strict group-based evaluation provides reliable non-contact body weight estimation on unseen livestock.
Navsari Agricultural University Universiti Putra Malaysia Kamdhenu University
Fatimah Khalid（First author）
Livestock Body Weight Prediction Mask R-CNN XGBoost Weighted Ensemble CatBoost
Frontiers in Marine Science Sep 28, 2026
Splitting the East China Sea into clear and turbid regimes by nLw555, a CatBoost reconstruction finds the turbid zone is a weak CO2 source and that conventional models overestimate the ECS sink by about 46%
Using a threshold of nLw555 = 1.5 mW cm-2 µm-1 sr-1 from MODIS to separate the East China Sea into Clear Water (CW) and Turbid Water (TW), this study reconstructed surface pCO2 for 2003–2023 with a CatBoost model that treats water type as a categorical input alongside multi-spectral satellite variables, achieving independent-test accuracy of R2 = 0.86 and RMSE = 16.55 µatm and cutting TW RMSE by up to about 81% relative to conventional models; the 21-year climatology gives CW 361.2 µatm and TW 415.1 µatm, with TW acting as a weak net source (+0.50 Tg C yr-1) driven by positive non-thermal anomalies (+28.09 µatm), implying conventional models overestimate total ECS carbon uptake by roughly 46% (about 4.04 Tg C yr-1).
Using a threshold of nLw555 = 1.5 mW cm-2 µm-1 sr-1 from MODIS to separate the East China Sea into Clear Water (CW) and Turbid Water (TW), this study reconstructed surface pCO2 for 2003–2023 with a CatBoost model that treats water type as a categorical input alongside multi-spectral satellite variables, achieving independent-test accuracy of R2 = 0.86 and RMSE = 16.55 µatm and cutting TW RMSE by up to about 81% relative to conventional models; the 21-year climatology gives CW 361.2 µatm and TW 415.1 µatm, with TW acting as a weak net source (+0.50 Tg C yr-1) driven by positive non-thermal anomalies (+28.09 µatm), implying conventional models overestimate total ECS carbon uptake by roughly 46% (about 4.04 Tg C yr-1).
Pusan National University National Fisheries Institute
Do-Hyeon Kwon（First author）
CatBoost sea surface pCO2 reconstruction air-sea CO2 flux estimation optical water classification East China Sea
medRxiv Sep 14, 2026
Diagnostic Value of Large Language Model-Extracted Gross Brain Findings in Neurodegenerative Diseases
Using 5,613 autopsy cases from the Mayo Clinic Brain Bank collected between 1998 and 2023, this study fine-tuned a large language model to convert narrative gross descriptions into semi-quantitative scores for 39 features (extraction accuracy 0.95 on 200 manually annotated feature-level test examples), then classified seven neuropathologic diagnostic categories with a CatBoost classifier and a second fine-tuned LLM, both including age at death, sex, and brain weight; on a held-out test set of 562 cases CatBoost reached accuracy 0.73, kappa 0.65, and macro-average AUC 0.92, while the text-based LLM reached accuracy 0.75 and kappa 0.68, with macro-average sensitivity 0.66 for both, PSP sensitivity 0.92 and 0.93, MSA sensitivity 0.87 and 0.92, but AD-LBD sensitivity only 0.21 and 0.
Using 5,613 autopsy cases from the Mayo Clinic Brain Bank collected between 1998 and 2023, this study fine-tuned a large language model to convert narrative gross descriptions into semi-quantitative scores for 39 features (extraction accuracy 0.95 on 200 manually annotated feature-level test examples), then classified seven neuropathologic diagnostic categories with a CatBoost classifier and a second fine-tuned LLM, both including age at death, sex, and brain weight; on a held-out test set of 562 cases CatBoost reached accuracy 0.73, kappa 0.65, and macro-average AUC 0.92, while the text-based LLM reached accuracy 0.75 and kappa 0.68, with macro-average sensitivity 0.66 for both, PSP sensitivity 0.92 and 0.93, MSA sensitivity 0.87 and 0.92, but AD-LBD sensitivity only 0.21 and 0.
University of Pennsylvania Mayo Clinic Institute of Science Tokyo
Daisuke Ono（First author）Shunsuke Koga（Corresponding author）
Gross brain findings Large Language Model CatBoost

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Extracting heart girth and body length from 2D images with age and sex, XGBoost predicts body weight in unseen Surti goats at R²=0.7955

Splitting the East China Sea into clear and turbid regimes by nLw555, a CatBoost reconstruction finds the turbid zone is a weak CO2 source and that conventional models overestimate the ECS sink by about 46%

Diagnostic Value of Large Language Model-Extracted Gross Brain Findings in Neurodegenerative Diseases