Lake of the Woods phyto- and picoplankton: spatiotemporal patterns in blooms, community composition, and nutrient status
Bibliographic record
Abstract
Watson SB and Kling H. 2017. Lake of the Woods phyto- and picoplankton: spatiotemporal patterns in blooms, community composition, and nutrient status. Lake Reserve Manage. 33:415–432.This study evaluated the phytoplankton and trophic status in Lake of the Woods (LOW; Ontario, Canada), a large, multi-basin waterbody with marked gradients in water quality and annual cyanobacterial blooms. We combined a broad comparison of average total phosphorus (TP)–biomass relationships in LOW and other north temperate lakes with a detailed spatiotemporal analysis of net-, nanno-, and picoplankton, total bacterioplankton, nutrient status, and water quality across six hydrological sectors of the lake between 2008 and 2010. Average total phytoplankton biomass (TB) varied considerably among zones and years but, similar to TP, was highest in the shallower, more eutrophic southern zones with a generally lower than average TB/TP yield within the among-lakes dataset. Summer–fall blooms of N2-fixing cyanobacteria were dominated by Aphanizomenon across most sectors except in the north, where Dolichospermum predominated. Unlike the larger size fractions, patterns in picoplankton suggested non-nutrient constraints: phycoerythrin-rich picocyanobacteria (<2 µm) showed a distinct south–north increase correlated with water transparency, while the more abundant phycocyanin-rich fraction was related to dissolved organic material with no distinct spatial patterns. Bacterial numbers were higher in surface layers and significantly related to temperature but not nutrients; spatial distribution indicated they were not directly introduced from riverine inputs. Overall, planktonic P deficiency was higher in the north and increased in late summer concurrent with N co-limitation and increases in N2-fixers. The combined data suggest that P ultimately limits LOW plankton, but short-term N and Si deficiencies influence the plankton community composition.
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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 | 0.000 |
| 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".