The distribution of phytoplankton along trophic gradients and its mediation by available light in the pelagic zone of large eutrophic lakes
Bibliographic record
Abstract
We describe the pattern and the principal factors affecting the phytoplankton biomass–nutrient relationship in the pelagic zone of large lakes. The results showed that the phytoplankton abundance and biomass of Cyanophyta, Cryptophyta, and Pyrrophyta were significantly correlated with trophic states. The total phosphorus (TP)–biomass relationship curves showed that the increment of biomass with TP is weak at high TP levels. The decrease in biomass at the high end of the curves might be a synthesis of the pattern of responses of the major taxonomic groups (except cyanobacteria) to environmental variables. Light limitation might be one of the important factors causing the decrease in the TP–biomass curve at high TP concentrations. If the mean underwater available light is lower than ∼250 µmol photons·m–2·s–1, clear-water species decline and cyanobacteria become dominant. The responses to available light of these key species play a central role in modulating the biomass–nutrient relationship. Our results contribute to the understanding of this relationship in the pelagic zone of large eutrophic lakes and have important practical implications for lake management.
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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 | 0.000 | 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".