Dynamic Photophysiological Stress Response of a Model Diatom to Ten Environmental Stresses
Bibliographic record
Abstract
Stressful environmental conditions can induce many different acclimation mechanisms in marine phytoplankton, resulting in a range of changes in their photophysiology. Here we characterize the common photophysiological stress response of the model diatomThalassiosira pseudonanato ten environmental stressors and identify diagnostic responses to particular stressors. We quantify the magnitude and temporal trajectory of physiological parameters including the functional absorption cross‐section of PSII (σPSII), quantum efficiency of PSII, non‐photochemical quenching (NPQ), cell volume, Chla,and carotenoid (Car) content in response to nutrient starvation (nitrogen (N), phosphorus (P), silicon (Si), and iron (Fe)), changes in temperature, irradiance, pH, and reactive oxygen species (ROS) over 5 time points (0, 2, 6, 24, 72 h). We find changes in conditions: temperature, irradiance, and ROS, often result in the most rapid changes in photophysiological parameters (<2 h), and in some cases are followed by recovery. In contrast, nutrient starvation (N, P, Si, Fe) often has slower (6‐72 h) but ultimately larger magnitude effects on many photophysiological parameters. Diagnostic changes include large increases in cell volume under Si‐starvation, very large increases in NPQ under P‐starvation, and large decreases in the σPSIIunder high light. The ultimate goal of this analysis is to facilitate and enhance the interpretation of fluorescence data and our understanding of phytoplankton photophysiology from laboratory and field studies.
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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".