Influence of the zinc hyperaccumulator<i>Thlaspi caerulescens</i>J. & C. Presl. and the nonmetal accumulator<i>Trifolium pratense</i>L. on soil microbial populations
Bibliographic record
Abstract
Metal hyperaccumulator plants like Thlaspi caerulescens J. & C. Presl. are used for phytoremediation of contaminated soils. Since little is known about the rhizosphere of hyperaccumulators, the influence of T. caerulescens was compared with the effects of Trifolium pratense L. on soil microbes. High- and low-metal soils were collected near a zinc smelter in Palmerton, Penn. Soil pH was adjusted to 5.8 and 6.8 by the addition of Ca(OH)2. Liming increased bacterial populations and decreased metal toxicity to levels allowing growth of both plants. The effects of the plants on total (culturable) bacteria, total fungi, as well as cadmium- and zinc-resistant populations were assessed in nonrhizosphere and rhizosphere soil. Both plants increased microbial populations in rhizosphere soil compared with nonrhizosphere soil. Microbial populations were higher in soils planted with T. pratense, but higher ratios of metal-resistant bacteria were found in the presence of T. caerulescens. We hypothesize that T. caerutescens acidifies its rhizosphere. Soil acidification in the rhizosphere of T. caerulescens would affect metal uptake by increasing available metals around the roots and consequently, increase the selection for metal-resistant bacteria. Soil acidification may be part of the hyperaccumulation process enhancing metal uptake from soil.
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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 | 0.000 |
| 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".