Amplicon-Based 16S rRNA Inventory of a Laboratory-Cultivated Hypersaline Halophilic Biofilm from Indonesian Salt Ponds: A Descriptive Pilot of Bacterial–Archaeal Composition
Bibliographic record
Abstract
Hypersaline environments support microbial communities with remarkable adaptations to osmotic stress, yet the ecological organization of biofilm-associated microbiomes in these habitats remains poorly understood.This study presents the first amplicon-based 16S rRNA characterization of a laboratory-grown halophilic biofilm derived from Indonesian salt ponds.Biofilm biomass from salinity stages of approximately 2%, 20%, and 40% NaCl was pooled prior to DNA extraction to ensure sufficient yield; therefore, the present study provides a descriptive community inventory rather than comparisons among salinities or biofilm baseline inventory.High-throughput sequencing identified 196 OTUs (Good's coverage = 0.9997) with moderate-to-high richness across alphadiversity indices.The community was dominated by bacterial genera including Oceanicaulis, Bacillus, Halomonas, and Limimaricola, alongside archaeal taxa such as Haloferax and Halohasta.These taxa are discussed in relation to ecological functions reported in prior hypersaline biofilm studies; however, spatial stratification cannot be inferred from a composite sample.Overall, this work provides baseline taxonomic data for Indonesian salt-pond-derived halophilic biofilms and motivates future spatially resolved and functional validation.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".