Lessons Learned from a Suite of CFB Borden Experiments
Bibliographic record
Abstract
This article summarizes several of many field-based studies of subsurface contaminant transport conducted over the last 30 years at the Canadian Forces Base (CFB) Borden site. The field research initially consisted of extensive monitoring of a leachate plume from an abandoned landfill and its analytical and numerical modeling. Lessons learned from these initial studies led to the execution and interpretation of a variety of tracer tests involving conservative and reactive/organic solutes tests performed at various scales. The lessons learned from these tracer tests revealed a number of deficiencies in classical theories of contaminant dispersion and reaction processes as they occur in groundwater, and thus spawned a new era of process-oriented research within the hydrogeological community. The extensively monitored tracer tests were followed by controlled spills of organic contaminants to observe their subsurface movement and distribution as well as the emplacement of a variety of contaminant sources in the saturated and unsaturated zones to study the ambient transport of contaminants. The controlled spills and emplaced sources of various contaminants were then utilized for testing various active and passive remediation technologies. These studies have led to fundamental insights and lessons learned that have significantly contributed to research on contaminant transport in both the saturated and unsaturated zones. Over the years, data generated by the University of Waterloo (UW) researchers and their collaborators continues to be examined by various research groups and has led to additional new insights on subsurface transport of various chemicals.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".