Soil biogeochemical toxicity end points for sub-Antarctic islands contaminated with petroleum hydrocarbons
Bibliographic record
Abstract
Sub-Antarctic islands have been subjected to petroleum hydrocarbon spills, yet no information is available regarding the toxicity of petroleum hydrocarbons to these subpolar soils. The purpose of the present study was to identify soil biogeochemical toxicity end points for petroleum hydrocarbon contamination in sub-Antarctic soil. Soil from Macquarie Island, a sub-Antarctic island south of Australia, was collected and exposed to 10 concentrations of Special Antarctic Blend (SAB) diesel fuel, ranging from 0 to 50,000 mg fuel/kg soil, for a 21-d period. The sensitivity of nitrification, denitrification, carbohydrate utilization, and total soil respiration to SAB fuel was assessed. Potential nitrification activity was the most sensitive indicator of SAB contamination assessed for nitrogen cycling, with an IC20 (concentration that results in a 20% change from the control response) of 190 mg fuel/ kg soil. Potential denitrification activity was not as sensitive to SAB contamination, with an IC20 of 950 mg fuel/kg soil for nitrous oxide production. Nitrous oxide consumption was unaffected by SAB contamination. Carbohydrate utilization (respiration caused by sucrose) was a more sensitive indicator (IC20, 16 mg fuel/kg soil) of SAB contamination than total respiration (IC20, 220 mg fuel/kg soil). However, total soil respiration was a more responsive measurement end point, increasing soil respiration over a 72-h period by 17 mg of CO2, compared to a change of only 2.1 mg of CO2 for carbohydrate utilization. Our results indicate that IC20s varied between 16 to 950 mg fuel/kg soil for Macquarie Island soil spiked with SAB diesel fuel. These results indicate that current cleanup levels derived from temperate zones may be too liberal for soil contamination in sub-Antarctic islands.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".