An Ecosystem Approach: Strengthening the Interface of Science, Policy, Practice, and Management
Bibliographic record
Abstract
An ecosystem approach is a framework for collaborative research, governance, and management that focuses on protecting ecosystem health with resilience (i.e., the capacity to respond to disturbance or perturbation by resisting damage and subsequently recovering) and fostering resource conservation and sustainable use. In celebration of this 50<sup>th</sup> anniversary of the Canada-U.S. Great Lakes Water Quality Agreement and the “United Nations Decade on Ecosystem Restoration” (2021-2030), the Healthy Headwaters Lab of the University of Windsor’s Great Lakes Institute for Environmental Research, Aquatic Ecosystem Health & Management Society, and many partners: convened a 2022 international conference on the ecosystem approach that included six synthesis working groups, arranged 16 public workshops throughout the Great Lakes basin to get stakeholder feedback on ways and means of advancing an ecosystem approach in the 21<sup>st</sup> century, and performed a follow-up participant survey and literature review. Collectively, these four project elements provided an opportunity to learn from past and current experiences with ecosystem approaches and look to the challenges and opportunities that lay ahead to improve efforts in implementing ecosystem-based management across the Great Lakes and beyond, and to reap its many social, economic, and environmental benefits.
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 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 | 0.000 | 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 | 0.000 | 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 teacher head, 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".