Documenting Spatial and Temporal Variations of Subsurface Contaminates Using Tree Cores: Implications for the Design of Effective Waste Management Strategies
Bibliographic record
Abstract
Proper waste management has become a worldwide humanitarian topic, because of increased awareness of potential risks posed by unsound waste disposal to human health and the environment (El-Fadel et al., 1997; Rowe et al., 1997). In remote communities in Canada’s North, here defined as the part north of the southern limit of discontinued permafrost zone, landfills and/or dumps remain the most common methods employed for the disposal of solid waste, much as they do elsewhere (Bright et al., 1995; Zagozewski et al., 2011). In northern communities, landfills or dumps have received typically household and commercial/industrial wastes or waste rocks from mineral \nexploration and mining activities (Bright et al., 1995; ROLES, 2014; Government of Canada, 2015). Modern engineered landfills are designed to mitigate or prevent the adverse impacts of waste on the surrounding environment. However, the generation of leachate and gas remains an inevitable consequence of existing waste disposal practices and at any future landfill sites, and risks to public health and environment may arise if sites are not well-controlled (Sawhney and Kozloski, 1984; \nAllen, 2001; Christensen et al., 2001; Eggen et al., 2010). Consequently, the development of innovative locality-specific strategies and methods is crucial to ensuring efficient solid waste management and environmental protection
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".