Preliminary investigation of lake-use patterns in prairie lakes, stakeholder perceptions, and resulting management implications
Bibliographic record
Abstract
Nanayakkara L, Wissel B. 2017. Preliminary investigation of lake-use patterns in prairie lakes, stakeholder perceptions, and resulting management implications. Lake Reserv Manage. 49:49–61.To better understand use and perception of lakes in the prairie region of North America, we distributed 200 lake-use and management surveys at 9 lakes in southern Saskatchewan, Canada, during 2013 and received 65 responses. Survey results indicate recreational uses such as angling, swimming, and boating were most common. Anglers targeted walleye ∼3 times more than other taxa such as northern pike (Esox lucius) and yellow perch (Perca flavescens), and consumption or catch-and-release was based on species and size. Overall, pollution of waterbodies (76% of responses) and overfishing (60% of responses) were stated as issues of highest concern in response to an open-ended question, but results varied among user groups. When asked to rank a set of issues potentially related to lake health, urbanization ranked above both agriculture and climate change. Only about half of respondents had a general understanding of eutrophication and food-web composition, and anglers were unconcerned about overfishing and had little concern for critical matters such as climate change and invasive species. These results indicate a possible decoupling between science and public perception about anthropogenic influences on lake ecosystems in southern Saskatchewan prairie lakes. Importantly, 60% of respondents stated an interest in lake management involvement. Monitoring and committee participation garnered the most interest. Based on our results, scientific findings and future threats to lakes need to be better communicated to the public to successfully adapt to and mitigate the effects of important issues. Ultimately, enhanced science communication with stakeholders may increase citizen science involvement in lake monitoring and 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".