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From Algorithm to Expert:RLHF-Guided Vision-Language Model for 3D-EEM Fluorescence Spectroscopy Matching 认领 引用
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作者 Chenglong Lu Jiehui Li +5 位作者 Tonglin Chen Changhua Zhou Yixin Fan Xinlin Ren Ziyi Ju Wei Wang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1883-1900,共18页
Existing methods for tracing water pollution sources typically integrate three-dimensional excitationemission matrix(3D-EEM)fluorescence spectroscopy with similarity-based matching algorithms.However,these approaches ... Existing methods for tracing water pollution sources typically integrate three-dimensional excitationemission matrix(3D-EEM)fluorescence spectroscopy with similarity-based matching algorithms.However,these approaches exhibit high error rates in borderline cases and necessitate expert manual review,which limits scalability and introduces inconsistencies between algorithmic outputs and expert judgment.To address these limitations,we propose a large vision-language model(VLM)designed as an“expert agent”to automatically refine similarity scores,ensuring alignment with expert decisions and overcoming key application bottlenecks.The model consists of two core components:(1)rule-based similarity calculation module generate initial spectral similarity scores,and(2)pre-trained large vision-language model fine-tuned via supervised learning and reinforcement learning with human feedback(RLHF)to emulate expert assessments.To facilitate training and evaluation,we introduce two expert-annotated datasets,Spec1k and SpecReason,which capture both quantitative corrections and qualitative reasoning patterns,allowing the model to emulate expert decision-making processes.Experimental results demonstrate that our method achieves 81.45%source attribution accuracy,38.24%higher than rule-based and machine learning baselines.Real-world deployment further validates its effectiveness. 展开更多
关键词 Vision-language model reinforcement learning with human feedback pollution source tracing 3D fluorescence spectroscopy
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Biodiversity Risk and Opportunity Assessment in BAT Cooperative Tobacco-growing Areas 认领 引用
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作者 Long XU Jingming WANG +12 位作者 Kun FENG Yanfa CAI Bin LI Honghui YIN Dafei LI Lianchuan ZHOU Ying MA Gang WANG Pengcheng LIU Runtao LI Changhua ZHOU Ke YANG Jian CHEN 《Asian Agricultural Research》 2016年第2期28-29,33,共2页
Using the method in Biodiversity Risk and Opportunity Assessment Handbook of British American Tobacco Biodiversity Partnership,we assess biodiversity risks and opportunities in BAT and China's cooperative tobacco-... Using the method in Biodiversity Risk and Opportunity Assessment Handbook of British American Tobacco Biodiversity Partnership,we assess biodiversity risks and opportunities in BAT and China's cooperative tobacco-growing areas. The assessment results indicate that there are 8 risks and 1 opportunity. Action and monitoring plans have been made for medium and high risks as well as opportunity,to reduce impact on biodiversity. 展开更多
关键词 BAT Cooperative tobacco-growing area Biodiversity Risk and opportunity assessment
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