Biogeochemical toxicity and phytotoxicity of nitrogenous compounds in a variety of arctic soils
Bibliographic record
Abstract
Ammonium nitrate (NH(4)NO(3)) is a common water pollutant associated with many industrial and municipal activities. One solution to reduce exposure of sensitive aquatic systems to nitrogenous compounds is to atomize (atmospherically disperse in fine particles) contaminated water over the Arctic tundra, which will reduce nitrogen loading to surface water. The toxicity of ammonium nitrate to Arctic soils, however, is poorly understood. In the present study, we characterized the biogeochemical toxicity and phytotoxicity of ammonium nitrate solutions in four different Arctic soils and in a temperate soil. Soil was exposed to a range of ammonium nitrate concentrations over a 90-d period. Dose responses of carbon mineralization, nitrification, and phytotoxicity endpoints were estimated. In addition to direct toxicity, the effect of ammonium nitrate on ecosystem resilience was investigated by dosing nitrogen-impacted soils with boric acid. Ammonium nitrate had no effect on carbon mineralization activity and only affected nitrification in one soil, a polar desert soil from Cornwallis Island, Northwest Territories, Canada. In contrast, ammonium nitrate applications (43 mmol N/L soil water) significantly impaired seedling emergence, root length, and shoot length of northern wheatgrass (Elymus lanceolatus). Concentrations of ammonium nitrate in soil water that inhibited plant parameters by 20% varied between 43 and 280 mmol N/L soil water, which corresponds to 2,100 to 15,801 mg/L of ammonium nitrate in the application water. Arctic soils were more resistant to ammonium nitrate toxicity compared with the temperate soil under these study conditions. It is not clear, however, if this represents a general trend for all polar soils, and because nitrogen is an essential macronutrient, nitrogenous toxicity likely should be considered as a special case for soil toxicity.
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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 | 0.000 | 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 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".