Environmental drivers of phytoplankton community dynamics in an agriculturally-influenced tributary in the lower Great Lakes
Bibliographic record
Abstract
Phytoplankton community composition in tributaries differs from that in their receiving waters, due to light limitation from suspended particles and other factors such as nutrient availability and temperature. This study was designed to manipulate light levels in early, mid, and late summer to determine the combined effects of light attenuation and naturally varying nutrient availability on phytoplankton community composition in an agriculturally-influenced tributary of the lower Great Lakes. In all trials, in situ microcosm experiments show that phytoplankton abundance increased under three light attenuation treatments (60 %, 75 %, and 85 % attenuation) relative to time-zero, but higher light attenuation reduced total phytoplankton abundance relative to controls. Highest phytoplankton diversity in terms of richness and evenness occurred in September (late summer), and across all three trials was lowest under the highest light attenuation treatments (85 %). Phytoplankton community composition followed a normal seasonal shift from diatoms dominating in June (early summer), followed by cyanobacteria dominating in mid to late summer. In general, lower light levels (especially 85 % attenuation) corresponded with an increased dominance of cyanobacteria. These findings support the hypothesis that phytoplankton abundance and diversity vary with light and nutrient availability and that light attenuation promotes the shift from buoyant cyanobacteria to other taxa more tolerant of low light levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".