Controls on phytoplankton physiology in Lake Ontario during the late summer: evidence from new fluorescence methods
Bibliographic record
Abstract
Fast repetition rate fluorescence (FRRF) and spectral fluorescence, together with measures of nutrients and pigments, were used to characterize the composition and photosynthetic physiology of Lake Ontario phytoplankton in late summer and relate them to environmental conditions. Two stations demonstrated effects from relatively heavy anthropogenic disturbance and showed that the response of phytoplankton physiology to different impacts is highly variable. Other stations were more similar in phytoplankton composition, and in situ fluorescence yields ([Formula: see text]) in the lower surface mixed layer suggested good physiological condition (0.45–0.50). Nutrient ratios and mean irradiance indicated a general state of light saturation and slight phosphorus (P) deficiency, but physiological variations among stations were unrelated to measures of P deficiency. Fluorescence yields often decreased when surface layer samples were held in the dark, consistent with an induction of chlororespiration and prior exposure to supersaturating levels of irradiance. Comparative estimates of photosynthesis by FRRF and 14C revealed disparities suggestive of substantial differences between in situ and incubation methods, while spectral fluorescence appeared to underestimate cyanobacterial abundance. FRRF parameters, particularly [Formula: see text], were effective in identifying higher-impact stations and showed promise as an efficient means of characterizing variations in phytoplankton condition that may underlie phenomena such as taste and odour production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".