Masked-Reference Field Validation Artifacts for a Training-Free Micro-Edge Ag-IoT Data-Quality Framework

This record contains the reproducibility artefacts for the manuscript “Masked-Reference Field Validation of a Training-Free Micro-Edge Framework for Agricultural IoT Data Quality.” The study evaluates a frozen, training-free, and resource-aware micro-edge framework for Agricultural Internet of Things (Ag-IoT) data quality using a masked-reference field validation protocol. Trusted observed field values were temporarily hidden, repaired using nine methods, and then compared with their stored references to support direct field-based repair scoring. The repository includes MATLAB scripts, mask manifest, fixed seeds, configuration files, result workbook, figure source data, composite figures, supplementary figures, literature-screening file, dataset-source notes, run-level logs, and reproducibility notes. These artefacts support the reported analyses on reconstruction accuracy, plausibility violations, confidence, fallback behaviour, and replay-based resource indicators. Raw third-party datasets are not redistributed in this record. The Field Validation Pool (FVP) records were derived from three raw agricultural observation datasets archived in Zenodo: Raw Tomato Greenhouse Environmental and Plant Growth Dataset (https://doi.org/10.5281/zenodo.20096294), Raw Rock Melon Environmental and Plant Growth Dataset (https://doi.org/10.5281/zenodo.20096467), and Raw Chinese Kale Environmental and Plant Growth Dataset (https://doi.org/10.5281/zenodo.20096516). Users who wish to rerun the full workflow should obtain the raw datasets from the original Zenodo records or data owners. The masked-reference procedure scores hidden observed field values only. It does not claim to know the true values inside natural missing field outages. Therefore, the artefacts should be interpreted as supporting materials for field-based validation, reproducibility, and transparency.本存档包含论文《面向农业物联网数据质量的免训练微边缘框架的掩码参考域验证》的可复现性配套资源。本研究采用掩码参考域验证协议，对一款冻结式、免训练且资源感知的农业物联网（Agricultural Internet of Things, Ag-IoT）数据质量微边缘框架进行评估。研究中将可信实测场域值临时隐藏，通过九种方法完成修复，随后与存储的参考值进行比对，以支持直接基于场域的修复评分。 本仓库包含MATLAB脚本、掩码清单、固定随机种子、配置文件、结果工作簿、图表源数据、合成图表、补充图表、文献筛选文件、数据集来源说明、运行级日志及可复现性说明。上述配套资源可支撑针对重建精度、合理性违规情况、置信度、回退行为以及基于重放的资源指标的已报道分析。 本存档未重新分发第三方原始数据集。场域验证池（Field Validation Pool, FVP）记录源自存档于Zenodo的三份原始农业观测数据集：《原始番茄温室环境与植物生长数据集》（https://doi.org/10.5281/zenodo.20096294）、《原始厚皮甜瓜环境与植物生长数据集》（https://doi.org/10.5281/zenodo.20096467）及《原始芥兰环境与植物生长数据集》（https://doi.org/10.5281/zenodo.20096516）。如需完整复现工作流程，用户需从原始Zenodo存档或数据所有者处获取原始数据集。 掩码参考域流程仅对隐藏的实测场域值进行评分，并未宣称知晓自然缺失场域中断内的真实值。因此，本配套资源仅作为场域验证、可复现性研究及透明度提升的辅助材料。