Frustule‐related gene transcription and the influence of diatom community composition on silica precipitation in an iron‐limited environment
Bibliographic record
Abstract
A microcosm study in iron‐limited waters of the northeast subarctic Pacific Ocean was conducted to examine how iron availability affects the frustule‐related response of individual diatoms and thus the total quantity of silica precipitated by the community. New silica precipitated per cell was estimated using the fluorescent cell stain 2‐(4‐pyridyl)‐5{[4‐dimethylaminoethyl‐aminocarbamoyl)‐methoxy]phenyl}oxazole (PDMPO). Differences in new silica precipitation within a particular genus before and after iron enrichment were small compared to differences among genera, indicating that the quantity of total silica precipitated is particularly sensitive to community composition. Transcriptional patterns of genes encoding silicon transporters, aminopropyltransferases, chitin synthases, and a protein with uncharacterized function were measured in natural populations to identify indicators of the frustule‐related responses of different genera to iron limitation. Transcripts associated with silicon transporters were the most readily detectable in three metatranscriptome datasets and were capable of resolving species composition shifts and physiological responses. Silicon transporter transcripts from a distinct phylogenetic clade were most abundant in the iron‐limited community, and transcripts from a separate clade were more abundant in the community that bloomed after iron enrichment. Transcripts of the gene present in the iron‐limited community were also more abundant in iron‐limited laboratory cultures of Pseudo‐nitzschia multiseries , suggesting that this gene plays a role in silicon uptake during iron limitation. The responses of individual cells, as detected in this study, determine how the community influences silicon cycling in iron‐limited 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 | 0.000 | 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".