Changes in liquid water alter nutrient bioavailability and gas diffusion in frozen antarctic soils contaminated with petroleum hydrocarbons
Bibliographic record
Abstract
Bioremediation has been used to remediate petroleum hydrocarbon (PHC)-contaminated sites in polar regions; however, limited knowledge exists in understanding how frozen conditions influence factors that regulate microbial activity. We hypothesized that increased liquid water (θ(liquid) ) would affect nutrient supply rates (NSR) and gas diffusion under frozen conditions. If true, management practices that increase θ(liquid) should also increase bioremediation in polar soils by reducing nutrient and oxygen limitations. Influence of θ(liquid) on NSR was determined using diesel-contaminated soil (0-8,000 mg kg(-1)) from Casey Station, Antarctica. The θ(liquid) was altered between 0.007 and 0.035 cm(3) cm(-3) by packing soil cores at different bulk densities. The nutrient supply rate of NH 4+ and NO 3-, as well as gas diffusion coefficient, D(s), were measured at two temperatures, 21°C and -5°C, to correct for bulk density effects. Freezing decreased NSR of both NH 4+ and NO 3-, with θ(liquid) linked to nitrate and ammonia NSR in frozen soil. Similarly for D(s), decreases due to freezing were much more pronounced in soils with low θ(liquid) compared to soils with higher θ(liquid) contents. Additional studies are needed to determine the relationship between degradation rates and θ(liquid) under frozen conditions.
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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 | 0.001 | 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 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".