Comparative analysis of alkaline phosphatase-encoding genes (<i>phoX</i>) in two contrasting zones of Lake Taihu
Bibliographic record
Abstract
Limnetic habitats that are dominated by either algae or macrophytes represent the 2 dominant ecosystems in shallow lakes. We assessed seasonal variations in the diversity and abundance of alkaline phosphate-encoding genes (phoX) in these 2 zones of Lake Taihu, which is a large, shallow, eutrophic lake in China. There was no significant difference in seasonal mean phoX diversity between the 2 zones, whereas the seasonal mean phoX abundance in the macrophyte-dominated region was higher than that in the algae-dominated region. The bulk of the genotypes in the 2 regions were most similar to the alphaproteobacterial and betaproteobacterial phoX. Genotypes most similar to phoX affiliated with Betaproteobacteria were present with greater diversity in the macrophyte-dominated zone than in the algae-dominated zone. In the algae-dominated zone, the relative proportion of genotypes most similar to cyanobacterial phoX was highest (38.8%) in summer. In addition to the different genotype structures and environmental factors between the 2 stable states, the lower gene abundances and higher alkaline phosphatase activities in Meiliang Bay in summer than those in Xukou Bay reveals different organophosphate-mineralizing modes in these 2 contrasting habitats.
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 | 0.000 | 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".