Ecosystem health of Lake Vänern: Past, present and future research
Bibliographic record
Abstract
The large lakes of Sweden (Vänern, Vättern, Mälaren and Hjälmaren) have been monitored for more than four decades for water quality conditions to assess the impact of eutrophication from anthropogenic activities. Lake Vänern is basically an oligotrophic lake that showed signs of eutrophication, notably the emergence of algal blooms in the coastal areas (1967–1968). The lake was also contaminated, due to the discharge of pulp and paper effluents including metals such as mercury. However, ecosystem-based information is lacking for Lake Vänern. Consequently a symposium was organized in 2012 by the University of Gothenburg, Mötesplats Vänersborg and the Aquatic Ecosystem Health and Management Society to: assess the current status of the health of Lake Vänern's ecosystem, identify knowledge gaps and develop a road map for the future. In this regard, Lake Vänern was compared with the North American Great Lakes to learn from their extensive, long-term data sets. A special issue devoted to the “State of Lake Vänern Ecosystem” symposium was published in Aquatic Ecosystem Health and Management (AEHM, Vol. 17, no. 4) including keynotes and contributed papers. The conclusions shown in the appendix (available in the online supplementary information) summarize the authors' contributions. Most of the articles covered Lake Vänern, but some were directed towards monitoring and management of Great Lakes in general, and others addressed co-operation under the auspices of international agreements and directives. Based on the background information provided by the State of Lake Vänern Ecosystem symposium and its publication in the special issue, the conveners decided that a synthesis of the symposium with recommendations for the future would be useful in boosting ecosystem research in Lake Vänern.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".