Specification aggregate quarry expansion - A case study demonstrating sustainable management of natural aggregate resources
Bibliographic record
Abstract
Many countries, provinces, territories, or states in the European Union, Australia, Canada, the United States, and elsewhere have begun implementing sustainability programs, but most of those programs stop short of sustainable management of aggregate resources. Sustainable practices do not always have to be conducted under the title of sustainability. This case study describes how Lafarge, a large multinational construction materials supplier, implemented the principles of sustainability even though there was an absence of existing local government policies or procedures addressing sustainable resource management. Jefferson County, Colorado, USA, is one of three counties in the six-county Denver, Colorado, region that has potentially available sources of crushed stone. Crushed stone comprises 30 percent of the aggregate produced in the area and plays a major role in regional aggregate resource needs. Jefferson County is home to four of the five crushed stone operations in the Denver region. Lafarge operates one of those four quarries. Lafarge recently proposed to expand its reserves by exchanging company-owned land for existing dedicated open space land adjacent to their quarry but owned by Jefferson County. A similar proposal submitted about 10 years earlier had been denied. Contrary to the earlier proposal, which was predicated on public relations, the new proposal was predicated on public trust. Although not explicitly managed under the moniker of sustainability, Lafarge used basic management principles that embody the tenets of sustainability. To achieve the goals of sustainable aggregate management where no governmental policies existed, Lafarge not only assumed their role of being a responsible corporate and environmental member of the community, but also assumed the role of facilitator to encourage and enable other stakeholders to responsibly resolve legitimate concerns regarding the Lafarge quarry proposal. Lafarge successfully presented an enlightened proposal where the county will gain 745 acres of new open space land in exchange for 60 acres of current open space land adjacent to the quarry. The process involved collaborative efforts by all stakeholders and resulted in an outcome that balances the needs of society, the environment, and business.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".