{
  "id": 16355,
  "url": "https://link.springer.com/article/10.1007/s10506-026-09531-8",
  "title": "Intelligent support for soil and water conservation compliance: decision assistance capabilities of prompt-enhanced large language models",
  "summary": "Soil and water conservation (SWC) laws and regulations are hierarchically complex and frequently revised, and grassroots compliance urgently needs efficient, intelligent knowledge tools. Locally deployable small language models (SLMs) avoid the computational cost and data-leakage risks of large models, but their capability boundaries in the SWC legal domain and the pathways for enhancing them remain unclear. Using 71 national and local SWC regulations and the newly promulgated Ecological and Env",
  "authors": null,
  "category": "research",
  "topics": "regulation,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-04T00:00:00.000Z",
  "fetched_at": "2026-08-05T05:10:44.550Z",
  "source_slug": "x-artificial-intelligence-and-law",
  "source_name": "Artificial Intelligence and Law",
  "source_homepage": "https://link.springer.com/journal/10506",
  "ethics_ai_record_url": "https://ethics.ai/record/16355",
  "original_url": "https://link.springer.com/article/10.1007/s10506-026-09531-8",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}