Transcriptome-wide responses of aggregates of the diatom Odontella aurita to oil
Bibliographic record
Abstract
Diatom aggregates can play an important role in the formation of marine oil snow and the transport of oil from the sea surface to the benthos, yet their molecular response to oil has not been characterized. Here we use RNA-seq to analyze the transcriptome-wide responses of aggregates of the common Gulf of Mexico diatom Odontella aurita exposed to the water accommodated fraction of 2 different types of oil, Macondo surrogate and Refugio Beach oil. We identify a common set of 353 genes that are differentially expressed in response to both Macondo and Refugio oil exposure, relative to controls. Genes related to photosynthesis, and nuclear and ribosomal processes were all down-regulated in oil treatments, while genes related to repairing membrane damage, cellular stress, and the production of exopolymeric substances were up-regulated. Differential expression of genes was often greater in magnitude in the Refugio than the Macondo oil treatment, which may be due to differences in oil concentration in the treatments or to the physiochemical characteristics of the oils. Exposure to Refugio oil induced more severe nucleolar stress and more damage to chloroplasts than the lighter Macondo oil, which triggered an up-regulation of a more diverse suite of stress-response genes.
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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".