中国北方史前箭镞演化及其影响因素

Scientia Sinica(Terrae)（2023）

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Abstract

史前投射类工具的研究对理解史前人群的狩猎行为、生存策略及生业模式有重要意义.本文以史前投射类工具的主要组件——箭镞为研究对象,对中国北方地区旧石器时代中晚期至青铜时代箭镞的时空分布、形态演变、数量和材质变化等进行了梳理和分析,并对影响其变化的因素进行了探讨.分析结果显示,相对比较原始的石镞于旧石器时代中期开始零星出现,旧石器晚期的“广谱革命”和细石叶压制技术为宽厚型石镞在中国北方区域性发展提供了驱动力和技术支持;新石器时代,相对窄薄型的石镞和骨镞迅速扩散至整个黄河流域及东北的大部分地区,两者主体地位先后发生转换,且在7000～5000a BP期间数量达到最多,这可能与狩猎（渔猎）采集生业模式在社会经济中的地位变化、人口基数变化及磨制技术的出现与发展存在密切联系;青铜时代,大量青铜镞出现,箭镞总数再次回升且分布范围扩散至西北地区,而青铜冶炼技术的出现和群体间战争冲突可能是形成这一趋势的主要影响因素.研究显示,箭镞始终是史前人类生产生活工具组合中的一个重要组成部分,但受技术发展、生业模式演变等影响,箭镞的形态、功能及其流行性等在不同阶段发生了明显的变化.因此,对史前箭镞的系统研究,有助于深入理解史前人类社会的生业模式和经济发展过程.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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