Long-term adaptive response of an oceanic diatom to copper deficiency
Bibliographic record
Abstract
Enhanced vertical stratification brought about by warming of the ocean surface is expected to reduce vertical circulation and nutrient input with knock-on effects for phytoplankton. Increased nutrient limitation is one predicted outcome, but how that will impact phytoplankton is uncertain because we do not know how they will adapt. We used copper (Cu) as a model catalytic nutrient to explore the adaptive response of an oceanic diatom to continuous nutrient deprivation in laboratory experiments. Populations of Thalassiosira oceanica maintained under Cu-limiting and sufficient conditions for ~380 generations differed significantly in their abilities to grow in medium containing 1 nM Cu. Continued selection for more than 2000 generations increased Cu use efficiency (CuUE) of a low Cu-adapted (LCuA) population by more than 2-fold compared to the control and ancestral populations. The increase in CuUE resulted from a decrease in the amount of cellular Cu required for growth and an increase in the net carbon assimilation rate. Redistribution of cellular Cu and increased efficiency of photosynthetic reactions are hypothesized to explain the fast rates of maximum electron transport of low Cu-adapted cells despite containing less Cu. The results show that adaptation increased resource use efficiency in phytoplankton, which could reduce the impact of increased nutrient deficiency in the future ocean.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".