THE MECHANISM OF ISOTOPE FRACTIONATION DURING ALGAL NITRATE ASSIMILATION AS ILLUMINATED BY THE <sup>15</sup>N/<sup>14</sup>N OF INTRACELLULAR NITRATE<sup>1</sup>
Bibliographic record
Abstract
The 15N/14N of nitrate in the external medium and intracellular pool of the cultured marine diatom Thalassiosira weissflogii (Grun.) Fryxell et Hasle was measured during nitrate assimilation under low light, a 12:12‐h light:dark cycle, low temperature, or low iron conditions. The 15N/14N of the nitrate in the medium and the particulate matter both followed the predicted Rayleigh fractionation model, and the intracellular nitrate always had a higher 15N/14N than did the medium nitrate. When the experiments were compared, the results showed a negative correlation between the isotope fractionation factor and the difference in the 15N/14N between the two pools of nitrate. These observations imply that the variations in the isotope effect result from variations in the degree to which the fractionation by nitrate reductase is expressed outside the cell, which is, in turn, controlled by the rate of nitrate efflux relative to nitrate reduction. The low iron and low temperature experiments showed relatively small isotope effects but a large intracellular‐medium difference in nitrate 15N/14N, consistent with a relative rate of efflux (compared with influx) that is small and similar to fast‐growing cells. In contrast, large isotope effects and small intracellular‐medium differences in nitrate 15N/14N were observed for low light and light:dark cycle grown cells and are explained by higher relative rates of nitrate efflux under these growth conditions.
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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 | 0.001 |
| 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".