A comparative metagenomic and spectroscopic analysis of soils from an international point of entry between the US and Mexico
Bibliographic record
Abstract
The Paso del Norte region is characterized by its dynamic industries and active agriculture. Throughout the years, urban and agricultural soils from this region have been exposed to xenobiotics, heavy metals, and excess of hydrocarbons. In this study, samples of urban [domestic workshops (DW)] and agricultural-intended (AI) soils from different sites of Ciudad Juárez, Mexico were evaluated for their fertility, element content, and microbial diversity. Chemical analyses showed that nitrites, nitrates, P, K, Mg, and Mn were predominantly higher in AI soils, compared to DW soils ( p ≤ 0.05). The composition of soil microbial communities showed that Proteobacteria phylum was the most abundant in both soils (67%, p ≤ 0.05). In AI soils, Paracoccus denitrificans was reduced ( p ≤ 0.05), concurring with an increment in nitrates, while the content of nitrogen was negatively correlated with the rhizobium group ( r 2 = −0.65, p ≤ 0.05). In DW soils, the Firmicutes phylum represented up to ~25%, and the relative abundance of Proteobacteria strongly correlated with a higher Cu content ( r 2 = 0.99, p ≤ 0.0001). The monotypic genus Sulfuricurvum was found only in oil-contaminated soil samples. Finally, all samples showed the presence of the recently created phylum Candidatus saccharibacteria . These results describe the productivity parameters of AI soils and its correlation to the microbial diversity, which are very important to understand and potentiate the productivity of soils. The data also suggest that soils impacted with hydrocarbons and metal(oid)s allow the reproduction of microorganisms with the potential to alleviate contaminated sites.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".