Do human activities affect phytoplankton biomass and composition in embayments on Lake Diefenbaker?
Bibliographic record
Abstract
Lake Diefenbaker (LD) is an important source of water for southern Saskatchewan. LD is characterized by numerous embayments containing anthropogenic activities (e.g., housing, marinas, cattle watering). Many of these activities are increasing on this important reservoir in association with the rapidly developing economy of Saskatchewan. These activities may reduce water quality directly or indirectly by encouraging the growth of nuisance algae (i.e. cyanobacteria). Here, we examined phytoplankton biomass and composition in eight embayments exposed to anthropogenic activities, four unexposed embayments with no perceived human activities and six main channel sites adjacent to the embayments from June to October (2011 and 2012). Phytoplankton biomass and composition was not significantly different in exposed, unexposed embayments and main channel sites (p > 0.05), with the diatoms and cryptomonads constituting 87%–91% of the total phytoplankton biomass in both years. High flows from the South Saskatchewan River (SSR) in both years may have resulted in the rapid flushing of the embayments and dampened any localized impacts that could have resulted from anthropogenic activities as found in other studies. Hence, future study on LD should be conducted during years with low flow from the SSR when the rate of flushing of embayments will be reduced.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".