Effects of freshwater acidification and countermeasures
Bibliographic record
Abstract
Abstract Nowadays, pollution has become a serious problem with the development of industry and the exploitation of the earth’s resources. Acid rain and lake acidification caused by pollutants such as sulfur dioxide and nitric oxide has caused serious effects in many places. Nowadays people do not know the ecological influence of acidification, how to detect it and how it recovers. This paper examines the effects of acid rain on fish, plants and microorganisms in freshwater lakes, as well as how to detect acid rain and how to manage and recover from it. The results are not always clear, but there is a lot of evidence that acidification is changing lakes in many aspects, whether living or non-living things, small or widespread factors, acidified freshwater is no longer what it used to be. By examining those problems, people can protect the environment more effectively. Reducing the occurrence of acid rain and the damage it causes in the future. The significance of this paper is analyzing the ecological influence of acid rain, studying and discussing the negative impact on species, and giving some solutions for people, governments and companies for acidification. Furthermore, lake water self-cleaning is also considered in the solution as well. Acid rain causes acidification of the soil, which has a negative impact on agriculture. And it also damages the breeding environment of animals, reducing their reproductive success. For example, fish, microorganisms, and plants can be negatively affected by acidification of the lake water, even leading to extinction.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".