Bacterial Targets as Potential Indicators of Diesel Fuel Toxicity in Subantarctic Soils
Bibliographic record
Abstract
Appropriate remediation targets or universal guidelines for polar regions do not currently exist, and a comprehensive understanding of the effects of diesel fuel on the natural microbial populations in polar and subpolar soils is lacking. Our aim was to investigate the response of the bacterial community to diesel fuel and to evaluate if these responses have the potential to be used as indicators of soil toxicity thresholds. We set up short- and long-exposure tests across a soil organic carbon gradient. Utilizing broad and targeted community indices, as well as functional genes involved in the nitrogen cycle, we investigated the bacterial community structure and its potential functioning in response to special Antarctic blend (SAB) diesel fuel. We found the primary effect of diesel fuel toxicity was a reduction in species richness, evenness, and phylogenetic diversity, with the resulting community heavily dominated by a few species, principally Pseudomonas. The decline in richness and phylogenetic diversity was linked to disruption of the nitrogen cycle, with species and functional genes involved in nitrification significantly reduced. Of the 11 targets we evaluated, we found the bacterial amoA gene indicative of potential ammonium oxidation, the most suitable indicator of toxicity. Dose-response modeling for this target generated an average effective concentration responsible for 20% change (EC20) of 155 mg kg(-1), which is consistent with previous Macquarie Island ecotoxicology assays. Unlike traditional single-species tolerance testing, bacterial targets allowed us to simultaneously evaluate more than 1,700 species from 39 phyla, inclusive of rare, sensitive, and functionally relevant portions of the community.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".