Forest dynamics in heavily polluted regions. Report No. 1 of the IUFRO Task Force on Environmental Change.
Bibliographic record
Abstract
Abstract This book is the first volume in a new book series covering many areas of forestry research, published by CABI in association with IUFRO (International Union of Forestry Research Organizations), and also the first report from the IUFRO Task Force on Environmental Change. The book provides a state-of the-art assessment of the extent of air pollution impacts in heavily polluted regions, with case studies from Europe, North America and Russia. It includes a summary for policy makers, and is of interest to researchers and students of forestry, environmental science and pollution studies. The book is arranged in 13 chapters - the first is an introduction, the second provides background information on different types of air pollution and describes the main types of pollutants found in heavily industrialized regions, chapters 3-10 are case studies (3-6 from Russia, 7 from Ontario (Canada), 8 from central and eastern Europe, 9 from California (USA), 10 from the Mediterranean Region), chapter 11 provides an overview of some of the approaches that have been adopted to ameliorate the effects of air pollution in boreal and temperate forests, chapter 12 discusses international activities to reduce pollution at the regional scale, and the last chapter is the summary for policy makers. A subject index is included.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".