Release of Ni and Co by microbial activity in New Caledonian ultramafic soils
Bibliographic record
Abstract
A high positive correlation was found between extractable Ni and Co contents and microbial activity of 40 ultramafic soil samples from New Caledonia, suggesting a possible role of microorganisms in the release of these two metals. A saprolite (ultramafic subsoil) and a hypermagnesian brown soil were incubated 9 months in different conditions. Ni and Co release, measured by their extractability, occurred without reduction of soil pH but did not occur when the native microflora was eliminated by heat treatment. However, when autoclaved soil was re-inoculated with a pinch of the same nonheated soil, the release of metals was recorded. The concentrations of extractable Ni and Co were much higher in soils amended with organic compounds in which microbial activity was enhanced, but only if the soil was not heat treated. The presence of Grevillea exul, a metal-tolerant plant, stimulated the metal release process, but the stimulation was less effective than it was in the compost-amended soil without plant. The influence of the plant in extractable Ni and Co contents in this amended soil was not significant. The release of the two metals therefore seemed to be induced by the activity of specialized organotrophic microorganisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".