气候相关文献中社会临界点证据的自动检测与结构化：一个模块化人工智能框架
Automated Detection and Structuring of Social Tipping Point Evidence in Climate related Documents: A Modular AI Framework
- University of Oulu（奥卢大学）
- ITML CY（ITML CY公司）
- PredictBy Research and Consulting SL（PredictBy研究咨询有限公司）
- Verimpact（Verimpact公司）
- Epsilon International Ltd（埃普西隆国际有限公司）
- Politecnico di Milano（米兰理工大学）
- Inspiring Futures Europe（欧洲启迪未来机构）
- Centre for European Policy Studies（欧洲政策研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对气候文献中社会临界点证据分散且缺乏系统发现方法的问题，提出一个模块化Transformer框架，在段落层面检测并结构化证据，实验表明其性能优于现有方法。
AI中文摘要：
气候文献的增长速度已超过评审团队的阅读速度。这一差距对于环境社会临界点这一概念尤为重要，它指的是小变化触发社会系统快速、自我强化变化的阈值。此类转变的证据通常包含在较长文档中的一两段内。因此，现有的文本挖掘工具——按主题对整篇文档进行分类或突出孤立主张——使得大量重要证据缺乏系统的发现或组织方法。本文提出一个开放且模块化的基于Transformer的框架，在段落层面检测并结构化社会临界点证据。该框架将五个组件整合为单一可部署工作流：用于分割的DistilBERT边界分割器，用于检测的迭代增强RoBERTa分类器，用于重写每个检测段落以提高清晰度的Mistral 7B模型，用于根据五个已发表的社会临界点标准对段落评分的LLaMA 3.2 3B模型，以及用于语义检索的Milvus向量存储。系统封装在Streamlit界面中，并以MinIO对象存储为后端。在由GPT-4.1标注的163段落基准和专家评审的51段落集上评估，分割器在九项指标综合得分（6.137）上超越了三种竞争方法。调优后的RoBERTa模型在完整基准上达到71.4%的准确率，Cohen's kappa为0.337；在带标签的段落上达到87.5%的准确率，kappa为0.742，优于气候专用模型和未调优的语言模型。
英文摘要：
The climate literature has grown faster than review teams can read it. That gap matters most for a concept like the environmental social tipping point, the threshold at which a small change triggers rapid, self-reinforcing change in a social system. Evidence of this kind of shift is usually contained in one or two paragraphs within a longer document. As a result, existing text mining tools-which categorize entire documents by topic or highlight isolated claims-leave an expanding set of important evidence without any systematic method for discovery or organization. This paper presents an open and modular transformer-based framework that detects and structures social tipping point evidence at the passage level. The framework joins five components into a single deployable workflow: a DistilBERT boundary splitter for segmentation, an iteratively augmented RoBERTa classifier for detection, a Mistral 7B model that rewrites each detected passage for clarity, a LLaMA 3.2 3B model that rates the passage against five published social tipping point criteria, and a Milvus vector store for semantic retrieval. The system is wrapped in a Streamlit interface backed by MinIO object storage. Evaluated on a 163-passage benchmark labelled by GPT-4.1 and a 51-passage set reviewed by experts, the splitter surpassed three competing methods on a nine-metric composite score (6.137). The tuned RoBERTa model achieved 71.4 percent accuracy with a Cohen's kappa of 0.337 on the full benchmark, and 87.5 percent accuracy with a kappa of 0.742 on passages with labels, outperforming both a climate-focused model and untuned language models.
