Metagenomic analysis provides functional insights into seasonal change of a non-cyanobacterial prokaryotic community in temperate coastal waters
Bibliographic record
Abstract
The taxonomic compositions of marine prokaryotic communities are known to follow seasonal cycles, but functional metagenomic insights into this seasonality is still limited. We analyzed a total of 22 metagenomes collected at 11 time points over a 14-month period from two sites in Sendai Bay, Japan to obtain seasonal snapshots of predicted functional profiles of the non-cyanobacterial prokaryotic community. Along with taxonomic composition, functional gene composition varied seasonally and was related to chlorophyll a concentration, water temperature, and salinity. Spring phytoplankton bloom stimulated increased abundances of putative genes that encode enzymes in amino acid metabolism pathways. Several groups of functional genes, including those related to signal transduction and cellular communication, increased in abundance during the mid- to post-bloom period, which seemed to be associated with a particle-attached lifestyle. Alternatively, genes in carbon metabolism pathways were generally more abundant in the low chlorophyll a period than the bloom period. These results indicate that changes in trophic condition associated with seasonal phytoplankton succession altered the community function of prokaryotes. Our findings on seasonal changes of predicted function provide fundamental information for future research on the mechanisms that shape marine microbial communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".