The Effects of Climate Change on Aquatic Ecosystems in Relation to Human Health
Bibliographic record
Abstract
This review paper aimed to summarize the climate change impacts on water sources and their relation with human and ecosystem health and evaluate better management strategies. In aquatic environments, climate change causes alteration of biodiversity and species distribution, changes in the duration of biological functions, decreasing productivities, alteration in food web structures, as well as triggering the invasion of various species, and variation in the presence, abundance, and concentrations of various co-stressors. Since the beginning of the 20th century, the surface water temperature in the oceans has risen by about 1°C. Consequently, human well-being is directly and indirectly affected by these alterations. The World Health Organization (WHO) estimates 3.5 million people die from water-related diseases each year. It is projected that the number of water-related diseases will increase due to the effects of climate change. To cope with these problems, alternative water management strategies should be developed to have resilient water systems in terms of both ecological and technological perspectives. Thus, water management requires the cooperation of many sectors including citizens, institutions, public and private sectors, etc. within a multi-stakeholder approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".