Assessment of relative environmental risk from logyard run-off in British Columbia
Bibliographic record
Abstract
Run-off is generated at logyards at sawmills and dryland sorts when mobile water interacts with woody debris. Logyard run-off can contain a range of constituents with the potential to have an adverse impact on the receiving environment. Many geoclimatic, operational and physical factors contribute to the volume and characteristics of run-off, and a management tool to predict relative environmental risk from different sites would be of value. In this study, we attempted to develop such a tool. A survey was devised and distributed to logyard operators in British Columbia. The survey provided information on site characteristics, volumes and types of wood processed, operational practices, the incidence of run-off, run-off treatment practices, as well as the ultimate receiving environment. Qualitative and quantitative data from the survey were subjected to statistical analyses to: (1) determine the factors that contributed to risk; (2) assign relative risk ratings to each site; and (3) rank facilities according to their potential to impact the receiving environment. Multidiscriminant analysis was used to determine which factors were correlated to environmental risk posed by run-off. Eighty-nine percent (64/72) of the facilities had visible run-off. Sixty-six percent (42/64) of the facilities fell into the high risk category with the remaining 34% (22/64) being low risk. In order of importance, volume of wood stored onsite (largest contribution), frequency of run-off events and colour intensity of run-off (smallest contribution) were factors that significantly contributed to risk and were correlated positively. The methods employed in this study could be applied as management tools to identify sites for further assessment and evaluate the need for remediation. Key words: wood leachate, run-off, logyard, dryland sort, environmental impact, toxic, remediation
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".