Molecular bacterial diversity and distribution in waste from a steel plant
Bibliographic record
Abstract
We characterized the bacterial diversity of newly produced steelmaking wastes (NPSW) and steelmaking wastes deposited (SWD) in a restricted land area, generated by the siderurgic industry, using the 16S rDNA clone library approach. A total of 212 partial-length sequences were analyzed, revealing 123 distinct operational taxonomic units (OTUs) determined by the DOTUR program to 97% sequence similarity. Phylogenetic analysis of bacterial 16S rDNA sequences from the NPSW and SWD libraries demonstrated that Gammaproteobacteria, Betaproteobacteria, Alphaproteobacteria, Actinobacteria, Planctomycetes, Firmicutes, and Bacteroidetes were represented in both libraries. Deltaproteobacteria, Acidobacteria, Chloroflexi, Deinococcus-thermus, Gemmatimonadetes, and candidate divisions OP10 and OD1 were only present in the SWD library, and Nitrospira was only present in the NPSW library. The abundance of sequences affiliated with Gammaproteobacteria was high in both libraries. Six previously unclassified OTUs may represent novel taxa. Based on diversity indices (Simpson, Shannon-Weaver, Chao1, and ACE), the SWD library had a higher diversity. LIBSHUFF comparisons of the composition of the 2 libraries showed that they were significantly different. These results indicate that the bacterial communities in steelmaking wastes present high phylogenetic diversity and complexity. A possible association between the functional diversity and the bacterial communities' complexity requires further phenotypic investigation.
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 | 0.001 | 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".