Bibliographic record
Abstract
The achievement of sustainable development entails the integration of scientific approaches and environmental management, legislation, and policy making realities. Aquatic ecosystem management is a key element in this endeavour for sustainability, since water quality is an important prerequisite for human health and development. The health and integrity of aquatic ecosystems should therefore not only be regarded as important in its own right, but also be seen as an indicator, or barometer, of success in achieving sustainability.Addressing this issue of integration within environmental assessment as well as between scientific assessment and policy making strongly depends on spatial scale. Indeed, it seems likely that the accuracy of our comprehension of processes decreases with increasing spatial scale. Therefore, the “scale issue” raises, among others, two types of questions regarding (i) the appropriateness of methods to the stakes, and (ii) the relevance of either the quality criteria or the remediation objectives.Iterations between these scales followed by integration are necessary, and they need to be achieved consistently in order to produce relevant managerial approaches and to promote efficient remediation and preservation of aquatic ecosystems. This led the Aquatic Ecosystem Health and Management Society (AEHMS) to sponsor a conference in the fall of 2003 entitled ‘Scaling from local to global perspectives,’ with support from Cemagref, a French public research institute involved in environmental research. The French Ministry of Ecology and Sustainable Development, the Rhone-Mediterranee-Corse Water Agency, the Rhone-Alpes Regional Council and the Rhone department also sponsored the conference. The Lyon meeting was the seventh in a series of biennial conferences convened by the AEHMS across the world. Past conferences were held in Canada, United States, Germany, Portugal, Mexico, and The Netherlands.About 30 oral presentations were given during the conference including the Vallentyne plenary lecture and keynotes in the following sessions: physical, chemical and biological determinants of ecological functioningadvanced (bioassay) techniques and their managerial applicationsscientific inputs to environmental policies and managementIn addition to the oral presentations more than 40 posters were displayed at the conference. Soon after the meeting an electronic proceedings was posted on the Society's web site. A compendium of papers from the conference are included in this special issue encompassing a wide range of topics such as ecotoxicology, ecosystem functioning/integrity, biodiversity, non-indigenous species and applied approaches on waste management. We hope these papers will help students, scientists and managers in tackling the complex issue of “scale” which will continue to confront investigators in the future. We take this opportunity to sincerely thank all partners, sponsors, and members of scientific and Local Arrangements committees for their support and efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.011 |
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; both teacher heads agree on what is shown here.
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".