Plant growth, soil properties, and microbial community four years after thermal desorption
Bibliographic record
Abstract
Abstract The effects of thermal desorption (TD) on soil physical and chemical properties after crude oil contamination are recently well studied. However, there are limited field‐scale studies on long‐term soil biological property recovery such as microbial communities and plant growth, which are vital for meeting global agrosystem demands and restoring ecosystem health. This study describes the status of soil biological properties after 4 yr of crop production on oil‐contaminated cropland remediated via TD and a modified land farming technique. Plots were constructed in 2015 with native, uncontaminated topsoil (A); TD‐treated subsoil (TDU); untreated land‐farmed subsoil (SP); TDU + A (TDA), and SP + A (SPA) where soil ratios were 1:1 by volume, and composted manure (CM) was applied at 40 Mg ha‐1. After 3 yr of crop production (2019) grain sorghum [Sorghum bicolor (L.) Moench] was planted. Soil microbial community characteristics were assessed through phospholipid fatty acid analysis and by estimating mycorrhizal root colonization. Notably, inherent soil chemical and physical properties influenced the recovery of microbial communities in remediated soils. However, sorghum biomass production in TDU was 50 ± 9% greater than SP while the microbial abundance in these treatments remained similar. Mycorrhizal colonization variation likely reflected rhizosphere nutrient scarcity and not the interactions of either remediation strategy. Based on these results after 4 yr of cropping, TDU does not diminish soil microbial recovery, and when possible, blending TDU materials with topsoil provides the greatest level of recovery relative to topsoil only.
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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 | 0.001 |
| 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".