Biostabilization of Gold Mine Tailings: Co-Inoculation of Cyanobacteria Under Sterile and Non-Sterile Conditions
Bibliographic record
Abstract
Abstract Cyanobacterial crust formation has attracted attention for stabilizing erosion-susceptible soils in desert regions. However, limited information exists on its application in waste impoundments such as mine tailings. Identifying suitable inoculants with the ability to develop biocrusts in the more toxic conditions of mine tailings represents a challenge for exploiting this biotechnology for such applications. In this study, the performance of two nitrogen-fixing cyanobacteria (Anabaena sp. and Nostoc muscorum), individually and as a consortium, in creating biocrusts over gold mine tailings were investigated under sterile and non-sterile conditions. The results showed that Anabaena sp. and the co-inoculation of the species promoted higher synthesis of chlorophyll-a and total EPS compared to N. muscorum. The inoculated strains also exhibited different responses in the amount of the EPS fractions. The less soluble and more condensed tightly bound EPS represented a higher fraction of total EPS with co-inoculation and N. muscorum. With respect to wind erosion resistance and compressive strength of the biocrusts generated, co-inoculation showed better performance, followed by N. muscorum, while Anabaena sp. appeared to be less effective. The presence of indigenous microbial community within the tailings influenced the biostabilization performance of Anabaena sp., while the influence was insignificant under co-inoculation and N. muscorum. Overall, inoculating the cyanobacteria in a mixture with complementary traits (higher chlorophyll-a synthesis and total EPS secretion of Anabaena sp. vs. higher TB-EPS fraction and filamentous growth of N. muscorum) presented an effective strategy in the development of a resistant biocrust against wind erosion. With this inoculation strategy, the beneficial effects of the individual strains on biocrust formation could be combined, thus a comparatively stronger structure could be formed. Besides chlorophyll-a content, factors such as cyanobacteria morphology and EPS fractions would contribute to the biostabilization process. The results also suggested that sterilization of the tailings would influence the performance of cyanobacteria depending on the inoculant. Thus, the response of inoculants to other microbial communities should be considered prior to field-scale application.
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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) | 0.001 | 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 | 0.001 |
| 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".