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Record W4243671745 · doi:10.1201/9781003043461-49

Acid Rain

2020· book-chapter· en· W4243671745 on OpenAlexaboutno aff
Umesh Chandra Kulshrestha

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

After the industrial revolution, increased emissions of SO 2 and NO x 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 SO 2 and NO x emission control policies under various national and international cooperative programs. However, in the developing countries, SO 2 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 SO 2 and NO x 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 SO 2 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 CO 2 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.044
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0440.015

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 head0.011
GPT teacher head0.187
Teacher spread0.176 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2020
Admission routes1
Has abstractyes

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