Microbial Responses to Micronutrient Amendments in Oxygenated and Deoxygenated Waters of the Arabian Sea
Bibliographic record
Abstract
Metalloenzyme cofactors and oxygen conditions are crucial for microbial metabolism, yet their combined effects on microbial ecosystems remain unexplored. This study explores the impact of micronutrient amendments (Zn, Fe, Co and their combinations) on the microbial community composition in oxygenated (73 m) and deoxygenated (200 m) waters of the Arabian Sea. Through controlled microcosm experiment and 16S rRNA amplicon sequencing, we observed that micronutrients significantly alter nutrient concentrations and microbial dynamics. At 73 m, micronutrient treatments reduced nitrate, nitrite and ammonia levels, whereas at 200 m, they increased nitrate and silicate levels. Total bacterial counts (TBCs) were higher in all treatments at both depths, with Fe showing the highest counts. Alpha diversity indicated that Fe-amended flask increased microbial diversity the most at 73 m, while mixed treatments had a pronounced effect at 200 m. Taxonomic analysis revealed significant genus-level variations in both bacteria and archaea. One-way analysis of variance (ANOVA) confirmed micronutrient impacts on nutrients and TBC. Canonical correspondence analysis (CCA) and non-metric multidimensional scaling (NMDS) revealed distinct clustering based on oxygen conditions. These results confirm our hypothesis that micronutrient amendments in varying oxygen levels distinctly alter microbial community composition and nutrient cycling in marine environments.
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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".