Physiological and biochemical response of freshwater cryptomonads <i>(Cryptophyceae)</i> to Fe deficiency
Bibliographic record
Abstract
Cryptomonads show strong seasonal distribution in lakes yet little is known of their nutrient requirements. In this study, we examined the role of Fe nutrition in the growth and photosynthetic properties of two freshwater cryptomonads (Cryptomonas sp. UTCC 337 and C. erosa UTCC 446). Cryptomonas sp. appeared more tolerant to Fe deprivation compared with C. erosa. Growth rates calculated for Cryptomonas sp. provided 100 nM and 1 000 nM Fe did not vary (0.55 d(-1)). Only cultures provided 10 nM Fe displayed significantly lower rates of growth (0.26 d(-1)) and lower cellular yields of chlorophyll. In contrast, cultures of C. erosa provided 10 nM Fe failed to grow, whereas cultures provided 100 nM Fe exhibited a reduced rate of growth (30% reduction) and lower yields of cellular chlorophyll (19% reduction) compared to high Fe (1 000 nM) cultures. Photochemical competency, assessed by measuring DCMU-enhanced fluorescence, was high for cells of Cryptomonas sp. regardless of the level of Fe provided (F(v)/F(m) > 0.7). In contrast, photochemical competency was considerably reduced (F(v)/F(m) = 0.46) for C. erosa provided 100 nM Fe. Consistent with this, levels of the Fe-containing electron transfer catalyst ferredoxin were reduced by 2.5 times in C. erosa provided 100 nM Fe compared to Fe-replete cells. By comparison, ferredoxin levels varied only slightly in cells of Cryptomonas sp. provided either 100 nM or 1 000 nM Fe.
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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) | 0.001 | 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".