Rôle de différents compartiments microbiens (biofilms, matières en suspension, sédiments de surface) et de leurs constituants (bactéries, polymères extracellulaires et biominéraux) sur la méthylation et la réduction de HgII
Bibliographic record
Abstract
Monomethylmercury formation, the neurotoxic form of mercury, is mainly linked to anaerobic microbial activity. In order to assess the relative contribution of several microbial compartments (biofilms, raw water and sediment) we evaluated methylation of samples from ponds of temperate area (Lorraine, France) and from subarctic ponds (Nunavik, Quebec). Biofilms were not found to specifically promote mercury methylation, whereas sediment emerges as the main compartment involved in mercury methylation. The formation of methylmercury is positively linked to the temperature and to nutrients. Thus, by increasing the open water period, the water temperature and of the microbial activity, current climate changes may turn these ponds in preferential location for mercury methylation in the subarctic ecosystem. Finally, the reactivity of green rust, a mineral which can be produced by bacterial activity of environmental biofilms, may compete with mercury methylation by reducing HgII into Hg0
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".