Picophytoplankton during the ice-free season in five temperate-zone rivers
Bibliographic record
Abstract
Although picophytoplankton (PP) (0.2–2 µm) are ubiquitous in lakes and oceans, their importance in rivers has rarely been studied. We examined PP assemblages during the ice-free period in five rivers of a temperate region varying in trophic state (9–107 µg/L total phosphorus) and water discharge (1–87 m3/s). In these rivers, PP abundance reached concentrations as high as those observed in lakes and oceans (∼104–105 cells/mL). The highest density of PP (4.9 × 105 cells/mL) was observed in the most eutrophic river when the water temperature (28°C) and total phosphorus (293 µg/L) were highest. For the most part, PP abundance was dominated by non-phycoerythrin-containing cyanobacteria; phycocyanin-rich cells accounted for ∼75% of PP abundance in all the rivers. In multiple regression analyses, water temperature and nitrate concentrations explained about half of the variation in PP abundance across the rivers. Discharge had no effect on PP abundance or biomass, whereas it had a significant negative effect on total algal biomass among the rivers. The PP contribution to total chlorophyll-a averaged 27% (ranging 16–46%) and did not decline with increasing nutrients as found in lakes and oceans. The PP biomass from microscopic enumerations reached a maximum of 9% of total phytoplankton biomass, comparable with that observed in lakes. The results of this study demonstrate the importance of including picophytoplankton when analysing phytoplankton communities in rivers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".