Validating potential toxicity assays to assess petroleum hydrocarbon toxicity in polar soil
Bibliographic record
Abstract
Potential microbial activities are commonly used to assess soil toxicity of petroleum hydrocarbons (PHC) and are assumed to be a surrogate for microbial activity within the soil ecosystem. However, this assumption needs to be evaluated for frozen soil, in which microbial activity is limited by liquid water (θ(liquid)). Influence of θ(liquid) on in situ toxicity was evaluated and compared to the toxicity endpoints of potential microbial activities using soil from an aged diesel fuel spill at Casey Station, East Antarctica. To determine in situ toxicity, gross mineralization and nitrification rates were determined by the stable isotope dilution technique. Petroleum hydrocarbon-contaminated soil (0-8,000 mg kg(-1)), packed at bulk densities of 1.4, 1.7, and 2.0 g cm(-3) to manipulate liquid water content, was incubated at -5°C for one, two, and three months. Although θ(liquid) did not have a significant effect on gross mineralization or nitrification, gross nitrification was sensitive to PHC contamination, with toxicity decreasing over time. In contrast, gross mineralization was not sensitive to PHC contamination. Toxic response of gross nitrification was comparable to potential nitrification activity (PNA) with similar EC25 (effective concentration causing a 25% effect in the test population) values determined by both measurement endpoints (400 mg kg(-1) for gross nitrification compared to 200 mg kg(-1) for PNA), indicating that potential microbial activity assays are good surrogates for in situ toxicity of PHC contamination in polar regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".