A Mass Balance Model of Rubber Crumb Discharge from Sports Fields and Contribution to Zinc Contamination in Motions Creek, New Zealand
Bibliographic record
Abstract
Artificial sports fields are becoming more common to increase usage and decrease maintenance cost. Modern artificial sports fields use rubber crumb to cushion impact. The Auckland Council Parks Department commenced investment of NZ$85 million to convert 37 sports fields to artificial turf. Seddon Fields and Michael Avenue Reserve sports fields were converted to artificial turf in 2013. In New Zealand, rubber crumb is sourced from discarded tyres. However, tyre wear is a principal source of zinc contamination in the region. An initial mass balance model of rubber crumb was undertaken to assess the order of magnitude of rubber crumb likely discharged from the fields. Previous estimates of zinc sources, contribution and harbour modelling of zinc accumulation in Motions Creek and the Meola Reef area of the Waitemata Harbour are compared to the new source of zinc from Seddon Fields’ rubber crumb run-off. Initial estimates suggest that rubber crumb run-off from sports fields constitutes a small, yet appreciable, source of zinc that merits further assessment regarding local contaminant loads and potential methods to decrease rubber crumb reaching waterways. A potential sustainable reuse of discarded tyres may be exacerbating contamination of Auckland’s waterways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".