Microorganisms that participate in biochemical cycles in wetlands
Bibliographic record
Abstract
Several biochemical cycles are performed in natural wetlands (NWs) and constructed wetlands (CWs). Knowledge of microorganisms can be used to monitor the restoration of wetlands and the performance of wastewater treatment. The phylum Proteobacteria is the most abundant in NWs and CWs, which plays a role in nitrogen (N), phosphorus (P), and sulfur (S) cycles, and in the degradation of organic matter. Other phyla were present at lower abundance. Archaea participate in methanogenesis, methane oxidation, and methanogenic N2 fixation. S and P cycles are also performed by other microorganisms, such as Chloroflexi and Nitrospirae. In general, there is more information about the N cycle, especially nitrification and denitrification. Processes where archaea participate (e.g., methane oxidation and methanogenic N2 fixation) remain unclear, and several of these microorganisms have not been isolated so far. In this study, we used 16S rDNA or functional genes. The use of functional genes provides information to monitor specific microbial populations, and 16S rDNA is more suitable for taxonomic classification. In addition, several Candidatus microorganisms have not been isolated to date. However, their metabolic roles in the biochemical cycle of wetlands have been described.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".