Selenite transport and its inhibition in the unicellular green alga <i>Chlamydomonas reinhardtii</i>
Bibliographic record
Abstract
The influence of time, ambient concentration, and medium composition on selenite (Se(IV)) uptake by the unicellular green alga Chlamydomonas reinhardtii has been investigated. The aims of the performed experiments were to describe the kinetics of accumulation, to characterize transport capacities, to identify key nutrients influencing absorption, and to establish links between speciation and bioavailability. Our results suggested that the adsorbed fraction was negligible compared to the absorbed one. Over the short time scale considered, the absorption was linear with time, with an estimated conductance of approximately 0.2 nmol/m2/h/nM. Uptake was proportional to ambient levels in a broad range of intermediate concentrations (from nM to microM). However, conductances were higher at low concentrations (< nM) and then decreased with increasing concentrations (> microM). These results suggested that a specific but rapidly saturated transport system was involved at low concentrations, coupled with a nonspecific one that was only saturated at high ambient concentrations (approximately mM). The latter could involve transporters used by anionic macronutrients, which is supported by the fact that increasing sulfate and nitrate concentrations induced significant inhibition of Se(IV) uptake. Finally, Se(IV) speciation changes caused by varying pH did not significantly affect bioavailability.
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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".