Bibliographic record
Abstract
Introduction (Nigel Bell & Michael Treshaw) Historical Perspectives (Michael Treshaw & Nigel Bell) Emissions, Dispersion and Atmospheric Transformation (Roy Colville) Deposition and Uptake by Vegetation (David Fowler) Effects of Oxidants at the Biochemical, Cell and Physiological Levels with Particular Reference to Ozone (Stephen Long and Shawna Naidu) Effects of Oxidants at the Whole Plant and Community Level (Michael Ashmore) Nitrogen Oxides: Old Problems and New Challenges (Terry Mansfield) Effects of Sulphur Dioxide (Allan Legge & Sagar Krupa) Effects of Fluorides (Len Weinstein & Del McCune) Effects of Volatile Organic Compounds (Christopher Collins & Nigel Bell) Effects of Particulates (Andrew Farmer) Effects of Increased Nitrogen Deposition (Roland Bobbink & Leon Laners) Effects of Wet Deposited Acidity (Trevor Ashenden) Effects of Pollutant mixtures (Andreas Fangmeier, Juergen Bender, Hand--Joachim Weigel & Hans--Juergen Jager) Decline and Air Pollution: An Assessment of Forest Health in the Forests of Europe, the Northeastern United States and Southeastern Canada (John Innes & John Skelly) Effects of Acidic Deposition on Aquatic Ecosystems (Ronald Harriman, Ricahrd Battarbee & Don Monteith) Effects on Bryophytes and Lichens (Jeffrey Bates) Modifications of Plant Response by Environmental Conditions (Gina Mills) Air Pollutant -- Abiotic Stress Interactions (Alan Davison & Jeremy Barnes) Effects of Air Pollutants on Biotic Stresses (Walter Fluckiger, Sabine Braun & Erika Hiltbrunner) Effects of Air Pollutants in Developing Countries (Fiona Marshall) Air Quality Guidelines and their Role in Pollution Control Policy (Michael Ashmore) Air Pollution and Climate Change (Victor Runeckles) Future Research Priorities and Directions (Nigel Bell)
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".