Climate as a driver of increasing algal production in Lake of the Woods, Ontario, Canada
Bibliographic record
Abstract
Paterson AM, Rühland KM, Anstey CV, Smol JP. 2017. Climate as a driver of increasing algal production in Lake of the Woods, Ontario, Canada. Lake Reserv Manag. 33:403–414.Lake of the Woods (LOW) is a large, transboundary lake that straddles the provinces of Ontario and Manitoba, and the state of Minnesota. Although algal blooms have been reported in the lake since the early 1800s, monitoring data and anecdotal evidence suggest that toxic, cyanobacterial blooms have increased in frequency and intensity in recent years. However, total phosphorus inputs from the lake's primary tributary, the Rainy River, have declined significantly since the late 1960s. We explore this disconnect by examining spectrally-inferred determinations of chlorophyll a (Chl-a) in lake sediment cores, as a measure of past changes in aquatic primary production. Beginning in the late 1970s to early 1980s, inferred Chl-a increased at 5 impact sites in the north end of LOW that currently experience cyanobacterial blooms in late summer and autumn. In contrast, no change in Chl-a was observed at an oligotrophic reference site with much lower cyanobacteria biomass. At the impact sites, Chl-a generally showed no significant relationship to long-term trends in diatom-inferred total phosphorus concentrations, but was significantly and positively correlated to climatic variables, including mean annual air temperature at all sites and total annual precipitation at 4 sites. These data suggest that climate change may exacerbate algal blooms in this moderately-enriched lake. The results also show that the effects of climate change on aquatic production may be enhanced at sites with higher nutrient concentrations, likely because of positive feedbacks between cyanobacteria biomass, water temperature and nutrient availability. The impact of climate change should be considered carefully in future management initiatives.
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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".