FERRIC CHELATE REDUCTASE ACTIVITY AS AFFECTED BY THE IRON-LIMITED GROWTH RATE IN FOUR SPECIES OF UNICELLULAR GREEN ALGAE (CHLOROPHYTA)1
Bibliographic record
Abstract
Four species of green algae (Chlorella kessleri Fott et Novakova, Chlorococcum macrostigmatum Starr, Haematococcus lacustris [Girod-Chantrans] Rostaf., Stichococcus bacillaris Nag.) were grown in iron-limited chemostats and under phosphate limitation and iron (nutrient) sufficiency. For all four species, steady-state culture density declined with decreasing degree of iron limitation (increasing iron-limited growth rate), whereas chl per cell or biovolume increased. Plasma membrane ferric chelate reductase activity was enhanced by iron limitation in all species and suppressed by phosphate limitation and iron sufficiency. These results confirm previous work that C. kessleri uses a reductive mechanism of iron acquisition and also suggest that the other three species use the same mechanism. Although imposition of iron limitation led to enhanced activities of ferric chelate reductase in all species, the relationship between ferric chelate reductase activity and degree of iron limitation varied. Ferric chelate reductase activity in C. macrostigmatum and S. bacillaris was an inverse function of the degree of iron limitation, with the most rapidly growing iron-limited cells exhibiting the highest ferric chelate reductase activity. In contrast, ferric chelate reductase activity was only weakly affected by the degree of iron limitation in C. kessleri and H. lacustris. Calculation of ferric reductase activity per unit chl allowed a clear differentiation between iron-limited and iron-sufficient cells. The possible extension of the ferric chelate reductase assay to investigate the absence or presence of iron limitation in natural waters may be feasible, but it is unlikely that the assay could be used to estimate the degree of iron limitation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".