Role of different microbial compartments (biofilms, suspended matters, surface sediment) and some of them components (bacterial cells, extracellular polymeric substances and biominerals) on HgII methylation and reduction
Bibliographic record
Abstract
La formation de méthylmercure, la forme la plus toxique du mercure, est due à l’activité bactérienne anaérobie. Afin de connaître la contribution des compartiments microbiens (biofilms, eaux brutes, sédiments) dans la méthylation du mercure, nous avons évalué les vitesses de méthylation d’échantillons de mares de région tempérée (Lorraine) et subarctique (Québec, Canada). Si les bactéries des biofilms ne semblent pas plus méthylantes que d’autres, le sédiment apparait comme le compartiment le plus méthylant en lien avec la concentration en nutriments ainsi qu’avec la température. Ainsi, les changements climatiques actuels, en augmentant la température de l’eau et en favorisant l’activité biologique, peuvent faire de ces mares des sites préférentiels de la méthylation du mercure en milieu subarctique. Enfin, l’activité des biofilms a mené à la formation de rouille verte, un minéral capable de réduire HgII en mercure élémentaire, concurrençant ainsi la méthylation bactérienne
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 | 0.001 | 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".