Inoculation of Soil Cyanobacteria Improves Fertility of a Soil Mixed With Copper Tailing Sands
Bibliographic record
Abstract
Industrial activities such as mining provoke soil degradation which can be restored by applying cyanobacteria as inoculants to promote biocrust development. However, previous to the field applications, there is a need to determine the cyanobacterial suitable species under laboratory conditions. In this investigation, a culture of Trichormus sp. isolated from environmental samples was inoculated on a soil mixed with copper tailing sands. The establishment of the cyanobacteria on the substrate was determined by confocal microscopy, and germination and growth of Polypogon australis, a tailing storage facility (TSF) natural colonizer were determined. In addition, the total nitrogen content (N), phosphorus available (P) and organic matter (OM) content of the inoculated substrates was measured after 30 days. The results showed that the cyanobacterial culture increases the speed but not the cumulative germination of P. australis, and generates an increase of available P, total N and OM of the mixed soil. The improvement in soil fertility and stability of tailing sands supports the use of cyanobacteria to restore degraded or mining polluted soils.
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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 | 0.000 |
| 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".