Bibliographic record
Abstract
IntroductionEnvironmental degradation encumbers human life in many ways and out of this health is one of the prominent ways. Degraded environments affect health directly and adversely. It is well recognized that the main economic burden associated with water pollution is cost of pollutio n on human health. Adequate supply of fresh and clean drinking water is a basic need for all human beings on the earth, yet it has been observed that millions of people worldwide are deprived of this because they are not getting clean drinking water. Most of metropolitan cities are facing the problem of water pollution. Not only metropolitan cities while other big cities such as Kanpur, Allahabad, Varanasi, which are situated near to bank of Ganga, facing the problem of water pollution at an exorbitant level. In these cities marginalized population live near to the bank of Ganga. Fulcrum of all above explanation is that, these cities are equally facing the problem of water pollution as metropolitan cities are faced. Axiomatic problem is that, owning to above explained plight situation of exploitation and poor management of fresh water is taking place. Water quality continuous to deteriorate in the country is failing to reimburse with the norms of safe drinking water. Hundred millions of people suffer from ill health and morbidity due to environmentally degraded and contaminated water quality. The World Health Organization (WHO, 2002) estimates 54.2 million DALYs (Disability Adjusted Life Year Lost) lost worldwide per year due to vulnerable water. According to WHO , DALY's( Disability Adjusted Life Year ) is the sum of the years of potential life lost due to premature mortality and the years of conducive life lost due to disability. If no action is taken to address unmet basic human need for water as many as 135 million will die from these diseases 2020 (Glecik , 2002)Literature ReviewPoor health is directly linked with to human capital deficits that affect adversely to both present and future conduciveness in terms of wage or salary loss. It clears that Healthy population are more conducive population. Without a healthy and productive labor force, the process of economic growth cannot sustain in long run. Healthy population in any country depends upon the healthy natural environment specifically fresh air, pure water etc. but fast rate of urbanization is responsible for the quality degradation of the natural resources. As a nuance the problem of water pollution in urban cites is peculiarly augmented. So many studies are taken into account of water pollution which shows that how urbanization responsible for water scarcity, water pollution, and finally for increasing health problems. If we focus on the world condition about fresh water availability than we find that less than 3% of the world's water is fresh and the rest is sea water and undrinkable and Of this 3%over 2.5 %is frozen, locked up in Antarkitica, the Arcitic and glacier and not available to man. Fulcrum of above explanation is that only 0.5 % water is available for human. This sort of notion exhibit that our earth is made from water but the water which is available for human activities and usable is only .5% (report - facts and trends water, 2005).It is important to note that available 0.5 % fresh water is not equally distributed on earth. Means only nine countries possess 60% of the world's available fresh water supply: Brazil, Russia, China, Canada, Indonesia, U.S., India, Columbia and the Democratic Republic of Congo (report- facts and trends water, 2005). However, local variations within countries are also finding at wider scale. These all information clears that the problem of water scarcity is present at global scale because of unequal distribution of water. A country considered under the category of water crisis when the availability of water falls below 1000 cubic meters per person per year (Chandrika R., 2006). Historically, India has been well endowed with large Freshwater reserves, but the rapidly increasing population and over exploitation of fresh water from surface and groundwater sources over the past few decades has resulted in water scarcity problem in some regions (Water -The India Story, March 23, 2009). …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".