Eco-Logical: An Ecosystem Approach to Developing Transportation Infrastructure Projects in a Changing Environment
Bibliographic record
Abstract
The development of infrastructure facilities can negatively impact critical habitat and essential ecosystems. There are a variety of techniques available to avoid, minimize, and mitigate negative impacts of existing infrastructure as well as future infrastructure development. However, such techniques may not always provide the greatest environmental benefit or may do very little to promote ecosystem sustainability. Concern for ecosystem protection, along with legislation and policy initiatives aimed at fostering an ecosystem-based approach, led an Interagency Steering Team to collaborate over a three-year period to write Eco-Logical: An Ecosystem Approach to Developing Infrastructure Projects. The Steering Team shared a vision of an enhanced and sustainable natural environment combined with the view that necessary infrastructure can be developed in ways that are more sensitive to terrestrial and aquatic habitats. Eco-Logical encourages all partners involved in infrastructure planning, design, review, and construction to use existing flexibility in regulatory processes. The Eco-Logical publication puts forth a conceptual framework for integrating plans across agency boundaries and endorses ecosystem-based mitigation – an innovative method of mitigating infrastructure impacts in today’s changing environment. To test the concepts presented in Eco-Logical, the Federal Highway Administration‟s (FHWA) Office of Planning, Environment, and Realty initiated a grant program in 2007. Of the 40 applications from across the country, FHWA funded 14 cooperative agreements and 1 interagency agreement, totaling approximately $1.4 million. The number and diversity of applications indicate a changing climate in the field of transportation with a shift to more ecologically sensitive planning.\n The selected grant projects incorporate tools and techniques ranging from the integration of environmental considerations in the transportation planning process to the use of Geographic Information Systems (GIS) and public involvement to integrate infrastructure and conservation plans. For example, one project tests and demonstrates how interagency partnerships and a willingness to adapt existing processes can enhance cultural and environmental stewardship in the long-range transportation planning process. The grant recipients represent state and local departments of transportation, federal and state resource agencies, Metropolitan Planning Organizations (MPOs), local governments, Non-Governmental Organizations (NGOs), and one university. Initial findings from the grant program indicate a successful integration of ecologically sensitive principles into infrastructure planning and project development. By creating and using data-driven tools and processes, the Eco- Logical grant projects show that partnering with resource agencies and stakeholders early in the planning and project development processes enhances the preservation of high-functioning ecosystems.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".