Bibliographic record
Abstract
▪ Abstract The impacts of airborne pollutants have been studied in only a few groups of soil animals, notably protozoans, nematodes, potworms, earthworms, mites, and collembolans. Pollutants in the form of acid depositions, which contain SO42−, NOx, H+, heavy metals, and some organic compounds, are not homogeneously distributed on the landscape. Deposition patterns depend mainly on landscape configuration and plant cover. Airborne pollutants affect soil animals both directly and indirectly. Direct toxic effects are associated with uptake of free acidic water from the environment by some soil animals and with consumption of polluted food by others. Indirect effects are mediated primarily through disappearance or reduction of the food resources (microflora and microfauna) of soil animals, changes in organic matter content, and modification of microclimate. In the field, changes in competition among species are probably important factors that influence the soil animal community structure as well as the reactions of individual species to soil acidification or liming. The overall effect is a depauperation of soil with an attendant reduction in the rate of organic matter decomposition. We have provided five hypotheses, using soil fauna as indicators, to allow for quick evaluation of environmental changes caused by airborne pollutants.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".