17. The State of Belle Park
Bibliographic record
Abstract
Our group used the Belle Park Golf course, located at 713 Montreal Street, as our project site. The course is located on top of a pre-existing dump that served as the city’s waste disposal site between the early 1950’s until 1974, it still shows signs of leachate seepage. The wildlife issue we explored is the effect of this leachate on the surrounding ecosystem, including tree species, fish and mammals. We researched case studies in similar situations that have been done in the past as a base for our understanding. The community entity are working with is the Environment Division of the City of Kingston, in charge of monitoring the hybrid poplar trees planted along the fairways. They are used as a means to extract contaminants from the soil, based on their proven ability for rapid growth. We will evaluate the effectiveness of this technique along with the vertical leachate extraction wells on site. Also, our group hopes to obtain the history of the water sample data that is collected from monitoring wells around the site for our analyses and interpretation. The latest budget information shows that the golf course lost $203,000 last year. This has prompted city council to reconsider the future of the property and open the doors for new innovative ideas. As part of our project we hope to come up with a sustainable solution that preserves the natural habitat and serves the community, such as a park.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.105 | 0.019 |
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".