Bibliographic record
Abstract
After the industrial revolution, increased emissions of SO2 and NOx from fossil fuel combustion have resulted in an acid rain problem. Earlier, acid rain was observed as a common phenomenon in North America and Europe, but recent studies show its spread in East Asia too, covering China, Japan, and Thailand. European data show that most of the acidity was intensified during 1955–1970, with a sudden increase in the mid-1960s. Scandinavia and Central and Southern Germany were among the worst-hit areas, whereas northeastern United States and southeastern Canada were the most affected areas in North America. Acid rain resulted in loss of fish population in the lakes of Sweden, parts of southwest Norway, and eastern North America. In parts of Germany and other European countries, forest damage and loss of needles from pine and spruce trees was noticed. Beginning with the 1972 Conference on the Human Environment in Stockholm, successful efforts have been made by North America and Europe to control acid rain through SO2 and NOx emission control policies under various national and international cooperative programs. However, in the developing countries, SO2 emissions are still on rise to achieve developmental targets. After the United States and Europe, China is the biggest consumer of fossil fuel where rapid increase in SO2 and NOx emissions is reported. South Asia is relatively safe from acid rain problems because of high buffering capacity of local dust in the atmosphere, which reacts with SO2 and forms calcium sulfate. Ultimately, this results in higher pH of rain water. Similarly, acid rain is not an immediate problem in other parts of the world. However, consequences of increasing consumption of fossil fuel to meet energy demand in developing regions need to be monitored through national and international network programs. Apart from acidification of oceans by CO2 rise, acid rain can also add to the process of acidification of coastal oceans, which might be damaging to the marine ecosystem in the future.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.091 | 0.031 |
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; both teacher heads agree on what is shown here.
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".