Acid deposition in the eastern United States and neural network predictions for the future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Back-propagation type neural networks were trained on total sulphur dioxide emissions from power plants and measured field data on precipitation chemistry. These trained networks were then able to predict seasonal changes in sulphate, hydrogen, nitrate, and ammonium ion concentrations caused by projected decreases in sulphur dioxide emissions from power plants in the eastern United States. Results showed that by 2010 the proposed reductions in sulphur dioxide emissions by the U.S. electric power utilities would just be sufficient to reduce acid rain conditions to the levels where human health problems are avoided. However, pollution from acid rain would still be impacting considerable regions of the north-eastern United States and south-eastern Canada causing other environmental damage such as loss of fish in acidic lakes. Key words: acid rain, desulphurization, modelling, neural networks, pollution.
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.
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 | 0.001 | 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) | 0.000 | 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 it