运用硼同位素进行古气候重建的研究进展

海水中硼的同位素组成受海水pH控制,海洋生物碳酸盐形成过程中海水的硼主要以B(OH)4-形式进入碳酸盐晶格,该过程不产生同位素分馏,因此海洋生物碳酸盐的硼同位素组成能够反映海水中B(OH)4-的硼同位素组成,进而指示当时海水的pH值,再根据海水与大气间CO2溶解平衡关系可以间接获得当时大气CO2分压。最近十多年来成功地运用生物碳酸盐硼同位素研究了过去20 Ma以来不同时间段古海水pH演化历史以及大气CO2分压的变化历史,使得硼同位素成为研究古气候的新方法。由于大气CO2分压对海水pH变化的响应非常灵敏,必须对各参数的取值进行严格的论证,而且模型本身的合理性及精确性也要进一步讨论。本文详细地介绍了硼同位素-pH方法的理论模型以及近年来运用该方法研究古气候取得的主要成果,同时对模型本身的研究进展也进行了阐述。     

汽车覆盖件冲压工艺设计KBE系统中的知识表示技术

根据汽车覆盖件冲压工艺设计的特点 ,阐述了与汽车覆盖件冲压工艺设计有关的不同类型知识的表示方法 ,主要有一阶谓词逻辑、语义网络、产生式规则、框架和剧本、可视化文本和图象 ,以及面向对象的方法等 ,并比较了各种方法的优缺点。面向对象的集成知识表示等方法成功地应用于汽车覆盖件冲压工艺设计KBE系统的知识表示     

EFFECTS OF WATERSHED REPRESENTATION ON RUNOFF AND SEDIMENT YIELD MODELING1

ABSTRACT: This paper evaluates the effects of watershed geometric representation (i.e., plane and channel representation) on runoff and sediment yield simulations in a semiarid rangeland watershed. A process based, spatially distributed runoff erosion model (KINEROS2) was used to explore four spatial representations of a 4.4 ha experimental watershed. The most complex representation included all 96 channel elements identifiable in the field. The least complex representation contained only five channel elements. It was concluded that oversimplified watershed representations greatly influence runoff and sediment yield simulations by inducing excessive infiltration on hillslopes and distorting runoff patterns and sediment fluxes. Runoff and sediment yield decrease systematically with decreasing complexity in watershed representation. However, less complex representations had less impact on runoff and sediment‐yield simulations for small rainfall events. This study concludes that the selection of the appropriate level of watershed representation can have important theoretical and practical implications on runoff and sediment yield modeling in semiarid environments.     

Geography and Recovery under the U.S. Endangered Species Act

Abstract: The U.S. Endangered Species Act (ESA) defines an endangered species as one “at risk of extinction throughout all or a significant portion of its range.” The prevailing interpretation of this phrase, which focuses exclusively on the overall viability of listed species without regard to their geographic distribution, has led to development of listing and recovery criteria with fundamental conceptual, legal, and practical shortcomings. The ESA's concept of endangerment is broader than the biological concept of extinction risk in that the “esthetic, ecological, educational, historical, recreational, and scientific” values provided by species are not necessarily furthered by a species mere existence, but rather by a species presence across much of its former range. The concept of “significant portion of range” thus implies an additional geographic component to recovery that may enhance viability, but also offers independent benefits that Congress intended the act to achieve. Although the ESA differs from other major endangered‐species protection laws because it acknowledges the distinct contribution of geography to recovery, it resembles the “representation, resiliency, and redundancy” conservation‐planning framework commonly referenced in recovery plans. To address representation, listing and recovery standards should consider not only what proportion of its former range a species inhabits, but the types of habitats a species occupies and the ecological role it plays there. Recovery planning for formerly widely distributed species (e.g., the gray wolf [Canis lupus]) exemplifies how the geographic component implicit in the ESA's definition of endangerment should be considered in determining recovery goals through identification of ecologically significant types or niche variation within the extent of listed species, subspecies, or “distinct population segments.” By linking listing and recovery standards to niche and ecosystem concepts, the concept of ecologically significant type offers a scientific framework that promotes more coherent dialogue concerning the societal decisions surrounding recovery of endangered species.     

A TREE-RING RECONSTRUCTION OF DROUGHT IN SOUTHERN CALIFORNIA1

ABSTRACT: Indices of annual diameter growth of trees were used to reconstruct drought in southern California back to A.D. 1700. A regional Palmer Drought Index served as predictand and tree-ring indices from eight sites as predictors in multiple linear regression analyses that yielded the prediction (reconstruction) equations. The regression explained 69 percent of the variance in Palmer Index in the period of calibration. The long-term reconstruction indicated that drought was rare in the first half of the current century relative to other discrete 50-year periods, and that based on evidence to date the last half of the 20th century may well turn out to be the most drought prone since A.D. 1700 in southern California.     

A Complicated Reef: The Relationship Between a Representation and the Represented Phenomenon

In this study, I describe the relationship between the Florida Aquarium's representation of a coral reef and the live reef that served as the model for the representation. I specifically illustrate how naming practices complicate the aquarium's stated purpose of environmental education. I conclude by suggesting ways the aquarium and other pro-environmental facilities can better manage environmental representation.     

洞穴次生化学碳酸盐沉积物-石笋的气候替代指标的意义与不确定性因素

洞穴次生化学碳酸盐(CaCO3)沉积物-石笋、钙华和流石,是一个具有丰富的陆地古气候信息的档案库。洞穴碳酸盐(CaCO3)-石笋古环境重建常用的代用指标有碳、氧稳定同位素、生长速度、纹层厚度和灰度、有机质荧光、微量元素等,利用多种替代指标的环境解译,增加了古气候环境重建的可靠性。本文从测年方法和石笋年代学的研究入手,对石笋的生长速度,沉积速率、纹层厚度、稳定同位素、微量元素等各代用指标指代的气候意义进行了简述,同时,也对洞穴次生化学碳酸盐(CaCO3)沉积物-石笋中各单一代用指标存在的不确定性影响因素进行了探讨。因此,我们认为洞穴动态监测可为洞穴碳酸盐(CaCO3)沉积物的各替代指标的解译提供可靠的依据,充分利用现代洞穴监测的碳酸盐(CaCO3)沉积物的各替代指标,并与现代器测气象资料进行相互对比、并用以校正,是精确或定量解释石笋气候替代指标的关键。     

基于BERT-BILSTM-CRF模型的电力行业事故文本智能分析*

为解决电力行业事故报告文本较长、语义复杂,难以进行有效文本识别问题,提出1种以BERT作为底层的预训练模型,并设计1种双重注意力机制编码器,结合BILSTM-CRF深度挖掘事故文本语义特征,从而实现文本智能分析。首先构建电力词典,通过对BERT预训练,进行BIO标注,然后引入BILSTM-CRF模型实现对文本标签智能分类,最后将该模型与现行其他4种深度学习模型进行对比。研究结果表明:该模型智能识别精确率、召回率及F1值(查准率)均达到约97%,较其他4种模型中效果最好的模型分别提高0.02,0.03,0.02。研究结果可为电力行业事故报告文本分析提供1种新思路。     

A review of selection‐based tests of abiotic surrogates for species representation

Because conservation planners typically lack data on where species occur, environmental surrogates—including geophysical settings and climate types—have been used to prioritize sites within a planning area. We reviewed 622 evaluations of the effectiveness of abiotic surrogates in representing species in 19 study areas. Sites selected using abiotic surrogates represented more species than an equal number of randomly selected sites in 43% of tests (55% for plants) and on average improved on random selection of sites by about 8% (21% for plants). Environmental diversity (ED) (42% median improvement on random selection) and biotically informed clusters showed promising results and merit additional testing. We suggest 4 ways to improve performance of abiotic surrogates. First, analysts should consider a broad spectrum of candidate variables to define surrogates, including rarely used variables related to geographic separation, distance from coast, hydrology, and within‐site abiotic diversity. Second, abiotic surrogates should be defined at fine thematic resolution. Third, sites (the landscape units prioritized within a planning area) should be small enough to ensure that surrogates reflect species’ environments and to produce prioritizations that match the spatial resolution of conservation decisions. Fourth, if species inventories are available for some planning units, planners should define surrogates based on the abiotic variables that most influence species turnover in the planning area. Although species inventories increase the cost of using abiotic surrogates, a modest number of inventories could provide the data needed to select variables and evaluate surrogates. Additional tests of nonclimate abiotic surrogates are needed to evaluate the utility of conserving nature's stage as a strategy for conservation planning in the face of climate change.     

Using abiotic variables to predict importance of sites for species representation

In systematic conservation planning, species distribution data for all sites in a planning area are used to prioritize each site in terms of the site's importance toward meeting the goal of species representation. But comprehensive species data are not available in most planning areas and would be expensive to acquire. As a shortcut, ecologists use surrogates, such as occurrences of birds or another well‐surveyed taxon, or land types defined from remotely sensed data, in the hope that sites that represent the surrogates also represent biodiversity. Unfortunately, surrogates have not performed reliably. We propose a new type of surrogate, predicted importance, that can be developed from species data for a q% subset of sites. With species data from this subset of sites, importance can be modeled as a function of abiotic variables available at no charge for all terrestrial areas on Earth. Predicted importance can then be used as a surrogate to prioritize all sites. We tested this surrogate with 8 sets of species data. For each data set, we used a q% subset of sites to model importance as a function of abiotic variables, used the resulting function to predict importance for all sites, and evaluated the number of species in the sites with highest predicted importance. Sites with the highest predicted importance represented species efficiently for all data sets when q = 25% and for 7 of 8 data sets when q = 20%. Predicted importance requires less survey effort than direct selection for species representation and meets representation goals well compared with other surrogates currently in use. This less expensive surrogate may be useful in those areas of the world that need it most, namely tropical regions with the highest biodiversity, greatest biodiversity loss, most severe lack of inventory data, and poorly developed protected area networks.