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Record W4245239147 · doi:10.1080/14634988.2014.966036

Preface

2014· article· en· W4245239147 on OpenAlexaboutno aff
M. Munawar, S.-Å. Wängberg, G. Dave, S. J. Blunt, Jamie Lorimer, M. Fitzpatrick, R. Rozon, Leanne E. Elder, Nigel S. Jarrett

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEutrophicationEnvironmental scienceFishingWater qualityFaunaBenthosLake ecosystemDreissenaGeographyFisheryHydrology (agriculture)EcosystemEnvironmental protectionEcologyNutrientGeology

Abstract

fetched live from OpenAlex

The large lakes of Sweden (Vänern, Vättern, Mälaren and Hjälmaren) were formed during the last glacial period when the ice retreated from central Sweden 10,000 years ago. These lakes constitute about 24% of the total lake area of Sweden and are extremely important as a water resource and are used for fishing, trade and recreational activities (Willén, 1984). At 5600 km2, Vänern is the largest lake within the European Union. Its location and size give the lake a maritime character with unique fauna and flora, which are recognised in several NATURA 2000 areas. Approximately 300,000 inhabitants live around the lake and depend on it as a source of freshwater. The lake is the largest water power regulation dam in Sweden, with a volume of 153 km3, and is commercially used both for transport and for fishing. It is also important for recreation for tourists and local residents.The large lakes of Sweden, including Lake Vänern, have been monitored for more than four decades for water quality conditions (phosphorus, nitrogen, plankton and benthos) to assess the impact of anthropogenic activities. Lake Vänern, historically characterized as oligotrophic, showed signs of eutrophication as early as 1967–1968 when conspicuous algal blooms were recorded in its bays. This lake was also affected by organic loading due to pulp and paper effluents which eventually coloured the water significantly. However, the discharge of organic substances was later reduced by 80% due to better management and improved water treatment practices. In addition, metal pollution from zinc mines in the north became a concern which was then reduced significantly, giving the lake an opportunity to recover.In 2012, a conference was organized (S.-A. Wängberg, G. Dave and M. Munawar, Co-chairs) on the State of Lake Vänern Ecosystem: Past, Present and Future (SOLVE) in Vänersborg, Sweden, from 11–14 June. The conference was organised specifically to improve understanding of the functioning of the lake, as well as to identify new and potential research areas for the future. Since the research activities on Lake Vänern are limited, the organizing committee made a concerted effort to invite keynotes from North American Great Lakes with long-term experience for exchange of ideas, approaches and techniques. It is hoped that some of the hypotheses, methods and technologies from the Great Lakes included in this special issue will be useful in developing future research and monitoring programs in Lake Vänern. The current special issue contains seven articles which focus on the state of this lake’s ecosystem as outlined below: Historical backgroundWater regulation and flood risk assessmentIncreasing algal biomass under decreasing phosphorusClimate variability and fish recruitmentSampling methods for littoral zone fishesCo-management of Atlantic Salmon and Brown TroutMulti-frequency acoustics for zooplankton monitoringThree invited keynote contributions (Minns, Nalepa and Munawar) tackle the following topics based on their long-term experience of the Great Lakes: Management of Great Lakes fisheriesComparison of invasive speciesGreat Lakes in transition: Changes at the base of the food webThe final two articles cover the timely topics of international management and agreements: European Water Frame DirectiveAquatic ecosystems across boundariesWe hope that this set of 12 articles dealing with Lake Vänern and the North American Great Lakes, as well as international agreements, will be useful to students, managers and researchers in improving their understanding of Lake Vänern, and in helping in the design of better research programs for the future. We would sincerely like to thank the members of the scientific committee for their hard work in developing an interesting conference program. Thanks are also due to Jessica Lindskog Sultan, Per-Ola Ramussen and Per-Åke Warg for the excellent local arrangements. We greatly appreciate the support of the University of Gothenburg, Göteborg, Högskolecentrum Vänersborg, in Sweden, and the Aquatic Ecosystem Health & Management Society (AEHMS), Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.005

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.

Opus teacher head0.007
GPT teacher head0.220
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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