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Record W1980599933 · doi:10.1007/s11356-013-1943-z

Processes and environmental quality in the Yangtze River system

2013· editorial· en· W1980599933 on OpenAlexfundno aff
Henner Hollert

Bibliographic record

VenueEnvironmental Science and Pollution Research · 2013
Typeeditorial
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsnot available
FundersDalhousie UniversityRWTH Aachen UniversityBundesministerium für Bildung und Forschung
KeywordsChinaHydropowerWater resourcesDrainage basinPopulationWater qualityFlood controlGeographyWater resource managementInterbasin transferFlood mythEnvironmental scienceEnvironmental protectionHydrology (agriculture)Ecology

Abstract

fetched live from OpenAlex

The Yangtze River is a source of life and prosperity for China and the river basin is home to 400 million people, one third of the population of China. The river basin accounts for approximately 50 % of the national total runoff and 40 % of China’s freshwater resources, which makes the river basin the most important source for drinking water, supplying water to 186 cities. In addition, it is one of the major arteries of China’s inland water transportation. The fish fauna of the Yangtze catchment is one of the richest in the world, providing more than 70 % of the fishery production of China. Due to its rich natural resources, the river contributes a lot to China’s economic development. It serves as a key factor in inter-provincial commercial trade and the regional economy. The potential benefits even grew after the Three Gorges Dam began operating at full capacity after 2009, including flood control, energy production, and improved navigation. The river is also among the key locations for China’s agriculture and industry. In 2006, the river hosted 10,000 chemical enterprises—about half of the total number in China (cf., Floehr et al. 2013, this issue). However, the Yangtze River—as an ecosystem as well as a lifeline—is reported to have been drastically degraded during recent decades (Muller et al. 2008; Xie 2003). A recently published focus story in Science entitled “Trouble on the Yangtze” highlights the potentially serious consequences of proposed and completed hydropower dams on the Yangtze River for fish and cetaceans (Qiu 2012). As pointed out by Yang et al. (2012) in a reply to the above-mentioned article, water pollution is another important but largely ignored concern; rising contamination and hydrological changes in the Yangtze River accelerate the loss of local species. Discharge of industrial and urban wastewaters, along with the application of fertilizers, pesticides, and herbicides in farming, have negative impacts on the water quality (Muller et al. 2008; Yang et al. 2012; Jin et al. 2012). These impacts include release of heavy metals and persistent organic pollutants as PAHs, together with re-mobilization of polluted sediments. Due to its pollution, the Yangtze River was rated by the World Wildlife Fund as being amongst the top 10 rivers at risk in the world (Wong et al. 2007; Wang et al. 2012a). This special issue of Environmental Science and Pollution Research highlights selected papers presented at two International Workshops on “Processes in the Yangtze River System—Experiences and Perspectives, one held at RWTH Aachen University, Germany in 2011, and the other at Tongji University in Shanghai, China in 2012. Both workshops were held for the purpose of scientific exchange on recent cuttingedge environmental research in the Yangtze River with a focus on the Three Gorges Reservoir (TGR). The second of these conferences, which is summarized in a conference report by Holbach et al. (2012a), marked two important milestones in Sino–German cooperation: (a) the 10-year anniversary of successful scientific Sino–German cooperation on questions related to the newly built Three Gorges Dam and (b) the midpoint of the Yangtze-Hydro Project (Bergmann et al. 2012) funded by the German Federal Ministry of Education and Research from 2010 to 2014. In detail, more than a dozen papers on processes and environmental quality in the Yangtze River basin are presented in this special issue elucidating the following topics:

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.015
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.325
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2013
Admission routes1
Has abstractyes

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