Impact of pH and salinity fluctuations on oxidation of Fe(II) by nitrate-reducing microorganisms enriched from the reduced tidal sediment of an extreme acidic river (Río Tinto, Spain)
Bibliographic record
Abstract
Nitrate reduction coupled to Fe(II) oxidation (NRFeOx) contributes to Fe cycling in the estuarian sediments of the Río Tinto river (Huelva, Spain). However, it is not yet known (i) whether and which NRFeOx microorganisms can be enriched from the reduced sediment layer and (ii) how in situ pH and salinity fluctuations affect NRFeOx. Therefore, we (i) used two different approaches, such as microcosm experiments (sediment amended with either NO3-/Fe2+aq or acetate/NO3-/Fe2+aq) and enrichment cultures (medium amended with acetate/NO3-/Fe2+aq) to enrich NRFeOx microorganisms to (ii) test their salinity and pH tolerance under simulated high tide and low tide conditions. We found that different microorganisms such as Thiobacillus (up to 9.7 ± 5.8% DNA-based 16S rRNA gene abundance) and Denitromonas (83.6% DNA-based 16S rRNA gene abundance) were contributing to NRFeOx in the microcosm experiments and enrichment culture approach, respectively. The strong buffering capacity of the native sediment and the presence of additional organic carbon as acetate can favor NRFeOx microorganisms during acidic water influx (low tide) events. The ∼100% conversion of NO3- to NO₂- under high tide conditions was observed both in the enrichment cultures and microcosm experiment when acetate was added suggesting the chemodenitrification may be the primary Fe(II) oxidation pathway under salty conditions.
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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) | 0.000 | 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) | 0.000 | 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".