Potential effects and impacts of a coal spill on sensitive aquatic habitat: a weight-of-evidence sediment quality assessment
Bibliographic record
Abstract
Abstract On 11 January 2014, a Canadian Pacific Railway train derailed on the Canadian National Railway Company's Yale Subdivision, Mile 122.7, in Burnaby, British Columbia, Canada. This derailment resulted in the partial release of metallurgical coal from three rail cars into, and adjacent to, Silver Creek. Following the derailment and subsequent spill, a comprehensive coal recovery program was implemented. As part of the program, coal deposits were removed from the Silver Creek mainstem in the right-of-way during the stabilization work. A total of approximately 143 tonnes of mixed coal, organic and mineral fines were removed during this program. Subsequently, using a weight-of-evidence sediment quality triad approach, a two-year Aquatic Impact Assessment was conducted to evaluate whether the remaining residual coal in Silver Creek and Burnaby Lake presented the potential for impact to the aquatic environment. Lines-of-evidence (LOEs) were evaluated, including sediment chemistry, sediment toxicity, bioaccumulation potential and coal content. The majority of the data from exposed sampling locations indicated that there was low potential for impact, based on the assessed LOEs. Hence, given the overall low potential for residual impacts from the coal deposits in the Silver Creek–Burnaby Lake ecosystem, no further clean up or monitoring was recommended.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".