Bibliographic record
Abstract
32-3 acid-neutralizing capacity (ANC) 70-72 acid rain effects on forests 70 effects on soils 69 failed regulatory efforts 78-9 mercury as stressor 70 over Eastern US 58 problem 61-8 possible solution to 80 Acid Rain Program (ARP) as alternative to TRI 53 conferring great latitude 63 critical load reporting 73 effects after implementation 60 emergence of 58 extensive data 67, 68 illustrating environmental regulation failure 80 limitations 71-2 overly positive assessment of 61 and public health 12, 61, 77 vs. railroad deregulation 62-3, 78 reason for inclusion 19 spurring technological innovation 78 success in reducing emissions 79 vs. TRI, information in 165 urban and rural areas 12, 77-8 Acid Rain Program Clean Air Interstate Rule (ARPCAIR) 71-2, 73 Ackerman, B. A. 8, 56, 62, 78 Adirondacks 61-2, 67, 70, 71, 80 agriculture 107-14 Air and Waste Management Association (AWMA) 51 Alberta, Canada 47-8, 51 Allwood, J. 124-5, 146, 186-7 aluminum containers 15, 125 industry study 37-8 manufacturing standards 38 prices 38 reductions dispute 15 in soils, surface water and effect on fish 69-71 American Coal Ash Association (ACAA) 15, 65 American Forest and Paper Association (AFPA) 133, 137, 154 Andreen, W. L. 87, 93, 171 AP-42 (emissions factor compilation document) 40, 43-4, 47 Appalachia 8, 58, 61-2, 67, 68, 71-2, 73-4, 80, 93, 175 aquatic effects of acid deposition 70-71, 79, 80 aquatic species 67, 69, 70, 92, 168 ARP see Acid Rain Program Association of Irritated Residents (AIR) 8 atrazine 176, 188 BACT (best available control technology) 7 baghouses 56, 57, 64, 66 bans see outright bans Barcott, B. 9, 10 Benford's Law 37 benzene emissions 36-7, 43, 45, 47-8 Berenyi, E. 141-2 Berglund, C. 135, 147 best available control technology (BACT) 7 best available technology (BAT) 11, 115, 166, 171-3 best management practices (BMP) 16, 91, 100, 102, 106-07, 114, 116, 118, 170-76 Bhopal, India 27
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.773 | 0.665 |
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".