{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T08:21:33Z","timestamp":1776759693998,"version":"3.51.2"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"European Union through the FUTURAL Project-Empowering the FUTure through innovative Smart Solutions for rURAL areas","award":["101083958"],"award-info":[{"award-number":["101083958"]}]},{"name":"Unitatea Executiv\u0103 pentru Finant,area \u00cenv\u0103t,\u0103m\u00e2ntului Superior, a Cercet\u0103rii, Dezvolt\u0103rii Si Inov\u0103rii (UEFISCDI) through the Project FUTURAL-Solu\u0163ii inteligente inovatoare pentru zonele rurale","award":["020234823"],"award-info":[{"award-number":["020234823"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3568028","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T17:40:01Z","timestamp":1746726001000},"page":"84112-84129","source":"Crossref","is-referenced-by-count":1,"title":["Agora: A Distributed Language Model Framework With API-Call Support for Integrated Climate Forecasting"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2133-6299","authenticated-orcid":false,"given":"Alexandra","family":"Udrescu","sequence":"first","affiliation":[{"name":"Computer Science Department, National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1521-5649","authenticated-orcid":false,"given":"Dan-Matei","family":"Popovici","sequence":"additional","affiliation":[{"name":"Computer Science Department, National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Internet Infrastructure","year":"2022"},{"key":"ref2","article-title":"Jointly preparing the conditions in the agricultural and connected sectors in the BSB area for the digital transformation (BSB smart farming)","year":"2024"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/715"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s43247-023-01199-1"},{"key":"ref5","first-page":"1","article-title":"ClimaX: A foundation model for weather and climate","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Nguyen"},{"key":"ref6","volume-title":"Futural Project\u2013agriculture and Climate","year":"2024"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref8","first-page":"1","article-title":"Toolformer: Language models can teach themselves to use tools","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Schick"},{"key":"ref9","volume-title":"KissanAI","year":"2024"},{"key":"ref10","article-title":"GPT-4 as an agronomist assistant? Answering agriculture exams using large language models","author":"Silva","year":"2023","journal-title":"arXiv:2310.06225"},{"key":"ref11","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv:2302.13971"},{"key":"ref12","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lewis"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1561\/1500000019"},{"key":"ref14","volume-title":"Gpt-j-6b: A 6 Billion Parameter Autoregressive Language Model","author":"Wang","year":"2021"},{"key":"ref15","volume-title":"Meta Releases Llama 3 Open-Source LLM","author":"Alford","year":"2024"},{"key":"ref16","first-page":"1","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hu"},{"key":"ref17","first-page":"10088","article-title":"QLoRA: Efficient finetuning of quantized LLMs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dettmers"},{"key":"ref18","volume-title":"Agri1","year":"2024"},{"key":"ref19","article-title":"Foundation models for weather and climate data understanding: A comprehensive survey","author":"Chen","year":"2023","journal-title":"arXiv:2312.03014"},{"issue":"2","key":"ref20","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. NaacL-HLT","volume":"1","author":"Devlin"},{"key":"ref21","article-title":"Retrieval-augmented generation for large language models: A survey","author":"Gao","year":"2023","journal-title":"arXiv:2312.10997"},{"key":"ref22","first-page":"1","article-title":"CRAFT: Customizing LLMs by creating and retrieving from specialized toolsets","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Yuan"},{"key":"ref23","first-page":"1","article-title":"ToolLLM: Facilitating large language models to master 16000+ real-world Apis","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Qin"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i16.29759"},{"key":"ref25","article-title":"Gorilla: Large language model connected with massive Apis","author":"Patil","year":"2023","journal-title":"arXiv:2305.15334"},{"key":"ref26","first-page":"1","article-title":"GPT4Tools: Teaching large language model to use tools via self-instruction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Yang"},{"key":"ref27","volume-title":"Vicuna: An Open-source Chatbot Impressing Gpt-4 With 90% Chatgpt Quality","author":"Chiang","year":"2023"},{"key":"ref28","article-title":"ToolAlpaca: Generalized tool learning for language models with 3000 simulated cases","author":"Tang","year":"2023","journal-title":"arXiv:2306.05301"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.462"},{"key":"ref30","first-page":"1","article-title":"Large language models as tool makers","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Cai"},{"key":"ref31","first-page":"1","article-title":"ToolkenGPT: Augmenting frozen language models with massive tools via tool embeddings","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","author":"Hao"},{"key":"ref32","first-page":"1","article-title":"Beyond the imitation game: Quantifying and extrapolating the capabilities of language models","volume-title":"Proc. Trans. Mach. Learn. Res.","author":"Srivastava"},{"key":"ref33","first-page":"1","article-title":"Augmented language models: A survey","author":"Mialon","year":"2023","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref34","article-title":"Multi-agent collaboration: Harnessing the power of intelligent LLM agents","author":"Talebirad","year":"2023","journal-title":"arXiv:2306.03314"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.701"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.614"},{"key":"ref37","article-title":"A survey on knowledge distillation of large language models","author":"Xu","year":"2024","journal-title":"arXiv:2402.13116"},{"key":"ref38","first-page":"1","article-title":"Self-refine: Iterative refinement with self-feedback","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Madaan"},{"key":"ref39","article-title":"Building guardrails for large language models","author":"Dong","year":"2024","journal-title":"arXiv:2402.01822"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10993389.pdf?arnumber=10993389","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T17:58:45Z","timestamp":1747677525000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10993389\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3568028","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}