Quantum efficiency of phytoplankton photochemistry measured continuously across gradients of nutrients and biomass in Lake Erie (Canada and USA) is strongly regulated by light but not by nutrient deficiency
Bibliographic record
Abstract
Unattended sensor networks are a cost-effective strategy to enhance the resolution of environmental datasets and are required to understand how large aquatic ecosystems respond to complex stressors (e.g., climate change). We made unattended and continuous measurements of the quantum yield of photosystem II ([Formula: see text]) photochemistry in the surface mixed layer of Lake Erie during three lake-wide cruises to observe how phytoplankton physiology varied across nutrient and taxonomic gradients. Three prominent diel [Formula: see text] patterns were noted. The diel maximum consistently occurred at sunrise or sunset, nocturnal measurements were consistently lower than diel maxima, and daytime values were strongly diminished by nonphotochemical quenching. The diurnal pattern was modeled as a function of irradiance to a mean accuracy of 0.03 to 0.04. Contrary to previously published reports in Lake Erie, [Formula: see text] was largely insensitive to indices of nutrient deficiency through space and time. This finding was consistent with much recent literature about [Formula: see text] and suggests that Lake Erie phytoplankton, like many others, can tune their photosynthetic machinery to maintain relatively high efficiency of photochemistry in photosystem II even when deficient in phosphorus or nitrogen.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".