Advancing an Ecosystem Approach in the Gulf of Maine
Bibliographic record
Abstract
Abstract.—The primary goal of ecosystem-based management (EBM) is to sustain the long-term capacity of the natural world to provide ecosystem services. A technical workshop was held on October 5, 2009 at the 2009 Gulf of Maine Symposium, with the objective of moving toward identifying, mapping, quantifying, and valuing ecosystem services in the Gulf of Maine. Ecosystem services are the benefits humans derive from ecosystems—the things we need and care about that we get from nature. Making the benefits of biodiversity and ecosystem services more apparent to environmental managers and society at large is necessary to pave the way for more efficient policy and management. Ecosystem services can provide a framework for assessing and resolving trade-offs among potentially conflicting human activities. Many of the scientific and technical elements necessary to move forward with ecosystem services approaches and EBM in the Gulf of Maine are already in place and have been applied in other areas. Currently, what is lacking is a policy and regulatory framework. Outstanding research questions include a more complete understanding of all ecosystem services, how they can be valued, and how the links within and among social-ecological systems influence their delivery. To implement ecosystem services and EBM in the Gulf of Maine, we need a clear vision, institutions with clear mandates, EBM science infrastructure, and integrative and interdisciplinary partnerships. Infrastructure for United States–Canada science coordination is in place through the Gulf of Maine Council and RARGOM (the Regional Association for Research on the Gulf of Maine). However, management issues are more difficult because we have bilateral agreements only on fish stock management, and we need formal agreements to work together on broader ecosystem elements.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".