Dual gradients of light intensity and nutrient concentration for full-factorial mapping of photosynthetic productivity
Bibliographic record
Abstract
Optimizing bioproduct generation from microalgae is complicated by the myriad of coupled parameters affecting photosynthetic productivity. Quantifying the effect of multiple coupled parameters in full-factorial fashion requires a prohibitively high number of experiments. We present a simple hydrogel-based platform for the rapid, full-factorial mapping of light and nutrient availability on the growth and lipid accumulation of microalgae. We accomplish this without microfabrication using thin sheets of cell-laden hydrogels. By immobilizing the algae in a hydrogel matrix we are able to take full advantage of the continuous spatial chemical gradient produced by a diffusion-based gradient generator while eliminating the need for chambers. We map the effect of light intensities between 0 μmol m(-2) s(-1) and 130 μmol m(-2) s(-1) (∼28 W m(-2)) coupled with ammonium concentrations between 0 mM and 7 mM on Chlamydomonas reinhardtii. Our data set, verified with bulk experiments, clarifies the role of ammonium availability on the photosynthetic productivity Chlamydomonas reinhardtii, demonstrating the dependence of ammonium inhibition on light intensity. Specifically, a sharp optimal growth peak emerges at approximately 2 mM only for light intensities between 80 and 100 μmol m(-2) s(-1)- suggesting that ammonium inhibition is insignificant at lower light intensities. We speculate that this phenomenon is due to the regulation of the high affinity ammonium transport system in Chlamydomonas reinhardtii as well as free ammonia toxicity. The complexity of this photosynthetic biological response highlights the importance of full-factorial data sets as enabled here.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".