Development and use of rapid reconnaissance soil inventories for reclamation of urban brownfields: A Vancouver, British Columbia, case study
Bibliographic record
Abstract
Iverson, M. A., Holmes, E. P. and Bomke, A. A. 2012. Development and use of rapid reconnaissance soil inventories for reclamation of urban brownfields: A Vancouver, British Columbia case study. Can. J. Soil Sci. 92: 191-201. As a result of suburban growth and abandonment and relocation of industrial facilities, vacant lots are becoming common in most urban centers in North America. These neglected, derelict, and often contaminated brownfields are receiving attention as a public liability since they are not productive and detract from the environmental quality of urban centres. Soils at these urban sites have been negatively impacted by anthropogenic activities. A prerequisite to effective reclamation is knowledge about the soil conditions on these sites. Most urban areas do not have soil survey or soil inventory information. Soil physical factors such as compaction are common problems at sites and are difficult and expensive to modify. A soil inventory provides the initial information for remediation and reclamation strategies that incorporate inherent soil properties. A soil inventory was conducted in Vancouver, British Columbia, by interpreting and extrapolating surficial geologic and regional soil survey information. The resulting soil inventory is presented as a series of topographical cross sections through the city, and displays information to stakeholders by reference to cultural features including street addresses. The soil inventory is compiled into soil management groups for general descriptions of the soil units and for initial recommendation for reclamation strategies.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".